The “hot cross bun sign” (HCBs) on magnetic resonance imaging (MRI) has been initially considered specific for multiple system atrophy with cerebellar features. However, a number of other conditions have since been described, which may be associated with this imaging sign. We herein describe a patient with anti-Ri and paraneoplastic cerebellar ataxia, and review the association of the HCBs on imaging with various neurological autoimmune conditions.We report a 40-year-old woman with anti-Ri-associated paraneoplastic neurological syndrome and breast carcinoma, in whom brain MRI revealed the HCBs late in the disease course. We also reviewed similar cases reported in the literature.via an axillary lymph node biopsy. Her symptoms partially resolved after the first corticosteroid pulse. However, subsequent immunotherapy and tumor treatments were ineffective. Four autoimmune cerebellar ataxia cases with the HCBs (two paraneoplastic and two non-paraneoplastic) were identified in the literature.The patient presented with cerebellar ataxia, polyneuropathy, and pyramidal signs. Although brain MRI was initially unremarkable, the HCBs and T2-weighted hyperintensity of the bilateral middle cerebellar peduncles were observed at later follow-up. Anti-Ri was detected in the serum and cerebrospinal fluid. Breast adenocarcinoma was confirmed The HCBs can be associated with paraneoplastic and non-paraneoplastic cerebellar ataxia, which may reflect neurodegeneration secondary to autoimmune injury. Thus, the HCBs should not be considered a contraindication for autoimmune cerebellar syndrome. The “hot cross bun sign” (HCBs) refers to a characteristic cruciform pontine T2-weighted hyperintensity evident on brain magnetic resonance imaging (MRI) and is suggestive of multiple system atrophy with cerebellar features (MSA-C). MSA-C is a neurodegenerative alpha-synucleinopathy and a common cause of adult-onset sporadic cerebellar ataxia . The diaA 40-year-old woman presented with paresthesia and weakness in all four limbs for 2 years and an unstable gait for 1 year. Electromyography conducted 7 months after the disease onset showed neurogenic change, while brain MRI performed was unremarkable. Ganglioside antibodies in the serum and cerebrospinal fluid (CSF) were both negative. Empirical corticosteroids significantly improved her symptoms. However, the numbness and quadriplegia reappeared during corticosteroid weaning 4 months later, and she gradually developed dizziness, an unsteady gait, and diplopia. Brain MRI performed at 16 months after the disease onset revealed T2 hyperintensity in the middle of the pons . There wPhysical examination revealed dysarthria, paresis of left eye adduction, diplopia, and nystagmus. The lower extremities exhibited weakness, hyporeflexia, and Babinski's signs. The finger-to-nose and knee-heel-shin tests revealed slowness and intentional tremors. She could not walk independently and was wheelchair-bound. Her sensation was intact. The Scale for the Assessment and Rating of Ataxia score was 33.5, and the modified Rankin Scale score was 4.Complete blood count, biochemical tests, and screening for infection, toxins, and metabolic and systemic autoimmune diseases were unremarkable. There was an increased CSF white blood cell count (22 cells/μL) and positive oligoclonal bands. PNS autoantibody assays (for the Hu/Yo/Ri/Ma2/Ta/CV2/Tr/Zic4/SOX1/amphiphysin antibodies) revealed anti-Ri in the serum and CSF. Electromyography demonstrated sensory polyneuropathy. Spinal MRI was unremarkable. Hypermetabolism and enlargement of the left axillary lymph nodes were apparent on positron emission tomography-computed tomography. Biopsies revealed breast adenocarcinoma metastases . No hypermetabolism in positron emission tomography-computed tomography was observed in the mammary glands, and pathological investigation after left mammectomy revealed no malignancy. 18F-fluorodeoxyglucose uptake of the cerebellum was decreased.The patient was diagnosed with PNS with breast carcinoma. Endocrine therapy with goserelin and letrozole and plasma exchange were performed. However, her neurological symptoms worsened. Follow-up brain MRI revealed the HCBs, with signal change in the bilateral middle cerebellar peduncles and widened cerebellar sulci . Her symHerein, we report a patient with paraneoplastic cerebellar ataxia and polyneuropathy in whom brain MRI showed the HCBs and bilateral middle cerebellar peduncle hyperintensities, imitating MSA-C. To our knowledge, this is the first report of the HCBs in a patient with PNS related to anti-Ri antibodies.As the targets of anti-Ri are intracellular RNA-binding proteins encoded by Nova-1 and Nova-2 genes, direct pathogenicity of anti-Ri antibodies is unlikely, and T lymphocyte-mediated neuronal damage is considered the main pathogenic mechanism . In pathHistorically, the HCBs was considered a marker of MSA-C . HoweverIn our systematic review, we searched the PubMed and EMBASE databases using “hot cross bun sign” OR “cruciform” OR “cruciate” and “autoimmune” OR “encephalitis” OR “rhombencephalitis” OR “paraneoplastic” as keywords. We excluded patients with central nervous system inflammatory demyelinating diseases because of their distinct pathogenicity. Four autoimmune cerebellar ataxia patients with the HCBs were identified in the literature . Two of In patients with PNS, the HCBs was first reported in a patient with kelch-like protein 11 (KLHL11) antibody . InteresThe patients with Homer 3 antibodies could show rapid eye movement sleep behavior disorder and cerebellar ataxia, in addition to the HCBs. Thus, it can be difficult to differentiate this disorder from degenerative MSA-C, particularly when the onset is insidious or an inflammatory CSF profile is absent , 28. AntThe HCBs usually appears late in the disease course, and patients generally experience poor outcomes despite immunological and oncological treatments. In our patient, immunotherapy and tumor treatment failed to improve the cerebellar syndrome. Nevertheless, stabilization of the ataxia and alleviation of CSF inflammation support an immune-mediated etiology and treatment efficacy. In these circumstances, irreversible neuronal loss secondary to autoimmune destruction of the cerebellum and related structures may have already occurred, as suggested by the prominent cerebellar atrophy and the HCBs on brain MRI. However, there is some evidence that the HCBs can be reversed after immunotherapy, such as in neuromyelitis optica spectrum disorders .This present case provides further evidence that the HCBs is not specific for MSA-C but, rather, can also appear in paraneoplastic or non-paraneoplastic autoimmune cerebellar ataxia, including PNS with anti-Ri. When this imaging characteristic is associated with the subacute/acute disease onset, an inflammatory CSF profile, and the absence of autonomic dysfunction , clinicians should consider the potential for autoimmune and paraneoplastic etiologies, and CSF examinations, neuronal autoantibody assays, and malignancy screening should be considered.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This study was approved by the Ethics Committee of Peking Union Medical College Hospital (JS-891). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.ML and HR drafted the manuscript for intellectual content and collected and analyzed the data. NL and YT collected and analyzed the data and revised the manuscript for intellectual content. SF and HG revised the manuscript for intellectual content. All authors contributed to the article and approved the submitted version.This study is funded by CAMS Innovation Fund for Medical Sciences (CIFMS #2021-I2M-1-003).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Despite the growing evidence of cognitive impairments in bipolar disorder (BD), little work has evaluated cognitive performances utilizing the latest version of the Wechsler Intelligence Scale-IV (WAIS-IV), which is one of the most widely used neurocognitive assessments in clinical settings. Furthermore, clinical characteristics or demographic features that negatively affect the cognitive functioning of BD were not systematically compared or evaluated. Accordingly, the present study aimed to examine the cognitive profile of bipolar I disorder (BD-I) patients and associated risk factors.via WAIS-IV, and clinical characteristics of the BD-I group were examined via multiple self- and clinician-report questionnaires.Participants included 45 patients, diagnosed with BD-I, current or most recent episode manic, and matching 46 healthy controls (HC). Cognitive performance was evaluated Multivariate analysis of covariance (MANCOVA) results indicated that the BD-I group demonstrated significantly poorer performance compared to the HC group in subtests and indexes that reflect working memory and processing speed abilities. Redundancy analysis revealed that overall symptom severity, manic symptom severity, and anxiety were significant predictors of cognitive performance in BD-I, while age of onset, past mood disorder history, depression severity, and impulsiveness showed comparatively smaller predictive values.The current study suggests cognitive deterioration in the cognitive proficiency area while generalized ability, including verbal comprehension and most of the perceptual reasoning skills, remain intact in BD-I. The identified risk factors of cognitive performance provide specific clinical recommendations for intervention and clinical decision-making. Bipolar spectrum disorders (BD) are one of the major chronic mental disorders with an aggregate lifetime prevalence of 2.4%, including bipolar I disorder (BD-I), bipolar II disorder (BD-II), and subthreshold BD . In recePrevious studies have shown specific neurocognitive areas of impairment, and researchers have used various cognitive and neurocognitive batteries for their studies . AlthougFurthermore, although there is accumulated evidence of cognitive impairments in a substantial number of patients with BD, key risk factors accounting for the specific cognitive deterioration remain unclear . For insThe present study seeks to investigate the cognitive profiles of patients with BD-I, current or most recent episode manic, and to compare the function between the BD-I and HC groups by utilizing the WAIS-IV to identify each cognitive area. Although previous research has evaluated neurocognitive deterioration in the BD group, there are few studies that have investigated the cognitive profile using the WAIS-IV , which is a widely used assessment tool that provides information on four areas of cognitive function: verbal comprehension, perceptual reasoning, working memory, and processing speed. The current study aimed to directly compare the cognitive profile between BD-I and a matching HC group. Specifically, this study only included BD-I patients whose most recent episode or current episode was manic, considering the known heterogeneity of BD group in terms of clinical and cognitive performances . Furthervia redundancy analyses (RDA).We hypothesized that (1) BD-I group would show a significant deterioration in working memory and processing speed ability regardless of demographic factors, while demonstrating intact verbal comprehension and perceptual reasoning ability compared to the HC group, and (2) clinical characteristics of both past history and current status would explain the specific cognitive function of BD-I group. In other words, we hope to uncover particular clinical characteristics from the heterogeneous predictor sets that are highly associated with specific cognitive areas from criterion sets, The current study was approved by the Institutional Review Board of the National Center for Mental Health  and written informed consent was obtained from all participants prior to enrollment.via advertisements, flyers, and local community support. As for the BD-I group, diagnostic interviews were administered to all potential participants for the HC group in order to carefully exclude individuals with any history of psychiatric disorders. A total of 46 participants completed a neurocognitive assessment.This study included two different groups, including 45 individuals diagnosed with bipolar I disorder (BD-I), current or most recent episode manic, and 46 healthy controls (HC) with no lifetime history of any psychiatric disorders. Participants of the BD-I group were recruited from the mood disorder clinic at the NCMH, and they were either in-patient or out-patient clients who contacted the clinic for intervention and/or comprehensive psychological assessment. The diagnostic evaluation was performed using the Mini International Neuropsychiatric Interview (MINI Plus), a semi-structured interview tool based on the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders DSM-IV; . DiagnosClinical Global Impressions Scale-Bipolar Version  that indicates the level of the individual’s overall cognitive function, General Ability Index (GAI) which is comprised of a Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI) scores, and Cognitive Proficiency Index (CPI) based on Working Memory Index (WMI) and Processing Speed Index (PSI) scores. There are four partial sub-indexes, including VCI, PRI, WMI, and PSI, and ten subtests; Similarities (SI), Vocabulary (VC), Information (IN), Block Design (BD), Matrix Reasoning (MR), Visual Puzzles (VP), Digit Span (DS), Arithmetic (AR), Symbol Search (SS), and Coding (CD). Finally, process scores were also included in the present study, which includes Block Design No Time Bonus (BDN), Digit Span Forward (DSF), Digit Span Backward (DSB), Digit Span Sequencing (DSS), Longest Digit Span Forward (LDSF), Longest Digit Span Backward (LDSB), and Longest Digit Span Sequencing (LDSS).t-test or chi-squared test, as appropriate. In order to evaluate group differences of WAIS-IV performance scores between the BD and HC groups, multivariate analysis of covariance (MANCOVA) was conducted after controlling the sociodemographic variables, which showed significant differences between the two groups. The partial η2 (denoted by η2p hereafter) value was presented to measure the effect size. Additionally, a post-hoc Bonferroni-corrected alpha level (p < 0.001) was applied to minimize the risk of a type I error when conducting multiple statistical tests.The frequency and descriptive analyses were conducted in the demographic analysis. Significant sociodemographic variables between BD and HC samples were compared using independent via WAIS-IV) and predictor  variables. Specifically, we divided the criterion variable into 3 sets to apply redundancy analysis. The first set includes all 7 index scores , the second set includes 10 subtests scores, and the last set includes 7 process scores. One common analysis is multivariate regression, which is essentially an ensemble of several multiple linear regression (n = 45). For this reason, we employed RDA, which can effectively reduce the number of functionally independent parameters , and thus appropriate for the analysis of small data. In this study, three sets of variables were created, with each set having multiple criterion variables and multiple predictor variables. Among these three sets, multiple predictor variables are all same across three sets while multiple criterion variables are different .To evaluate the clinical risk factors associated with cognitive impairment, redundancy analyses RDA;  were congression . Howeverrameters . Conceptrameters . In contrameters . MathemaThe IBM SPSS Statistics 27 program was used for most of the analyses other than redundancy analysis. Redundancy analyses were performed in SAS version 9.4 using the IML procedure.Sociodemographic characteristics of the BD-I and HC groups are presented in M = 22.80, SD = 6.34), duration of illness , type of the first episode , past history of hospitalization  and suicide attempts . Current status section includes the severity of BD sample’s current symptoms, which was evaluated based on the MINI’s standard of module D  manic episode) specifier , current suicide risk based on the MINI module C ‘s total points , and the scores on CGI-BP-depression , CGI-BP-mania , BRIAN , QUIDS , K-BAI , K-MDQ , BHS , and BIS-T  score. Lastly, the overall rate of current psychotropic medication is described. All of the BD-I participants were under psychotropic medication  and the following are information for each psychotropics; Second-Generation Antipsychotics , Mood Stabilizer , Benzodiazepine , and Antidepressant .Additionally, F = 2.68, p < 0.001, Wilk’s Lambda = 0.50, η2p = 0.50]. Then, each dependent variable was analyzed using the post-hoc Bonferroni-corrected alpha level p < 0.001. The results showed a significant main effect of group for most of the WAIS-IV scores. Specifically, the main effect of the group was significant for composite scores including FSIQ , GAI , CPI , PRI , WMI , and PSI . The VCI score did not show a significant difference between the two groups. Among the subtest scores, there was a significant main effect of group on VP , DS , AR , SS , and CD  scores. BDN , DSB  and LDSB  scores showed significantly lower performance in BD-I group among the process scores. All scores were significantly lower for the BD group than for the HC group. The results of the MANCOVA are reported in MANCOVA was conducted to identify whether the group differences exist on the WAIS-IV scores between the BD and HC group, even when controlling for the demographic covariates, age, and years of education. Results of MANCOVA showed that the dependent variables composed of WAIS-IV scores were significantly different between the two groups , Seoul, South Korea. Participants were provided with written informed consent to participate in this IRB-approved research project. The patients/participants provided their written informed consent to participate in this study.HK and WL jointly conceptualized the idea for research and designed the paper. HK analyzed the data, drafted the initial manuscript, and involved in the editing and revisions. DP and VR were responsible for resources allocation, funding acquisition, and provided intellectual input to the manuscript. JS was responsible for data analysis, statistical consultation, and contributed to initial manuscript drafting and editing. RY was responsible for data collection and management. WL contributed to manuscript editing, provided supervision, and was involved in editing and revisions. All authors contributed to the article and approved the submitted version.This research was supported by an intramural grant from the NCMH—No. 2019–01.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Data were analyzed using descriptivestatistics and a two-way analysis of variance. After adjustment for age,diagnosis of dementia and country of residence had significant effects on totalout-of-home participation (p < .01). The results contributeto policies and development of programs to facilitate social citizenship bytargeting specific activities and places.There is limited empirical knowledge about how older adults living with dementiaenact their social citizenship through out-of-home participation. This studyaimed: (a) to investigate out-of-home participation among older adults with andwithout dementia in four countries and (b) to compare aspects of stability orchange in out-of-home participation. Using a cross-sectional design, olderadults with mild-to-moderate dementia and without dementia, aged 55 years andover, were interviewed using the Participation in ACTivities and Places OUTsidethe Home questionnaire in Canada ( In demeA theory of social citizenship provides a lens, which helps understand the ways inwhich older adults, including those living with dementia, enact their agency andcitizenship though participation in “ordinary places” . Older aThe present study’s conceptualization of social citizenship is aligned with astrengths-based view of out-of-home participation that draws on the lived experienceof older adults and frames places outside the home as zones of mastery, opportunity,and challenge . This apBoth aging and living with a diagnosis of dementia can affect older adults’out-of-home participation . TheseaThe complexity of out-of-home participation suggests that individuals’ cognitivestatus and age need to be considered in relation to other factors potentiallyinfluencing out-of-home participation, such as the “living environment” .For theIn addition to structural and contextual aspects of participation in the livingenvironment, there are also social aspects to consider. Cultural norms and valuesconcerning age and dementia can impact out-of-home participation . For oldBased on our conceptualization and earlier research, in this study, we hypothesizedthat (a) older adults living with dementia would participate in fewer activities andplaces outside the home than older adults living without dementia; (b) older adultsliving with dementia would have abandoned a higher number of activities and placesoutside the home between the past and present than older adults living withoutdementia; and (c) that having a diagnosis of dementia and the country of residencewould have a significant effect on out-of-home participation, when controlling forage. Thus, this study aimed (a) to investigate out-of-home participation among olderadults living with and without dementia in four countries and (b) to compare aspectsof stability or change in out-of-home participation across those countries.A cross-sectional design was used to investigate the study aims and hypotheses infour countries, and the authors adhered to the Strengthening the reporting ofobservational studies in epidemiology (STROBE) checklist for cross-sectionalstudies . This stn = 28), Sweden(n = 35), Switzerland (n = 35) and theUnited Kingdom , and a further 163 older adultsliving without dementia  from Canada(n = 30), Sweden (n = 34), Switzerland(n = 35) and the UK (n = 64). The samplesize and power were calculated using an earlier study with the Swiss sample,indicating the need for a sample size of approximately 19 participants for eachsub-sample .A total of 325 older adults living with and without dementia were included inthis study  and the National Health Service (UK). The datacollectors used the brief cognitive screening tools that were commonly used inclinical practice at each recruitment site. Thus, the data collectors in Sweden,Switzerland, and the UK used the Montreal Cognitive Assessment (MoCA), whereasthe data collectors in Canada used the Mini-Mental State Examination (MMSE). Theolder adults living without dementia were recruited through senior centers(Canada), senior associations (Switzerland), and local networks, such ascommunity-based leisure and social groups . All participantsprovided their verbal and written informed consent for inclusion before theyparticipated. This research was granted ethical approval by the Office ofResearch Ethics at Simon Fraser University (2017s0052) for the Canadian sample,the Regional Board of Research Ethics (2015/77-31-5) for the Swedish sample, theCommission cantonale d’éthique de la recherche sur l'être humain in Lausanne(protocol 452/15) for the Swiss sample, and the Health Research Authority  for the UK sample. This studyadhered to the principles of the Helsinki Declaration .This paper used the Participation in ACTivities and Places OUTside HomeQuestionnaire (ACT-OUT) as a novel tool to explore social citizenshipthrough the older adults’ patterns of out-of-home participation inactivities and places . The ACTn = 6), (B) Places for medical care(n = 5), (C) Social, cultural and spiritual places(n = 6) and (D) Places for recreational and physicalactivities (n = 7). The participants responded yes (1) orno (0) to participating in each type of place in the past and present andthese responses were used to calculate total out-of-home participationscores out of a maximum of 24 places. The past and present time-points forparticipation were self-determined by each participant.The ACT-OUT questionnaire has three parts. Part one maps past and presentparticipation in 24 types of places and activities, part two elicits moredetail about the activities performed in two places for each domain, andpart three enquires about perceived risks when participating outside thehome. For the purposes of this study, only part one was utilized to mapparticipation according to four domains: (A) Consumer, administrative andself-care places  . These fData were collected between 2015 and 2017. For the ACT-OUT and demographicquestionnaires, the investigators elicited questions and recorded theparticipants’ responses using the data collection tools. Data were collectedusing face-to-face interviews at the participants’ homes or another locationof their choice. Participants could elect to have a significant other  presentfor support during the interview, but not for proxy-reporting. To ensure aflexible approach to meet the participants’ needs, abilities and routines,the interview procedure could be spread across three different sessions witheach session lasting under 90 minutes.Analyses first compared stability and change in out-of-home participationbetween the sub-samples of older adults living with and without dementia inthe four countries using descriptive statistics (hypotheses a and b). Thenext stage of analyses investigated the effects of the factors usingunivariate analyses and a multivariate model investigating whether having adiagnosis of dementia and the country of residence would have a significanteffect on out-of-home participation, when controlling for age (hypothesisc).p < .01 for allanalyses, including Bonferroni corrections. The results were interpretedaccording to the effect size thresholds for partial eta squared   .Preliminary tests revealed no violation of the assumptions to ensure the datafulfilled criteria for a two-way analysis of variance (ANOVA) i.e.,normality, linearity, homogeneity of variances or collinearity. One outlierwas identified; however, sensitivity analyses showed no significant effecton the results when the outlier was removed from the models; thus, it wasincluded in the analyses. Univariate analyses were performed, followed by abackward selection procedure to identify, and to remove, statisticallyredundant variables with a significance value less than .01 . Thebackward selection procedure was motivated by the lack of earlier researchregarding the relationship between the variables. In the final multivariatemodel, we conducted a two-way ANOVA to investigate the main and interactioneffects of diagnosis group and country of residence on total out-of-homeparticipation, when controlling for age group . MoreoveWe begin by presenting the background characteristics of the two sub-samples. Next,we present the descriptive results in relation to hypotheses a and b, followed bythe main results from the statistical model, to address hypothesis c.Total out-of-home participation was significantly lower for the sub-sample ofolder adults living with dementia compared to the sub-sample of older adultsliving without dementia. For both sub-samples, present participation generally decreased from pastparticipation across the 24 place types. F  = 4.46, p = .004,although the effect size was small . Furthermore,there was a statistically significant main effect on total out-of-homeparticipation for diagnosis group, F  = 53.63,p < .001, with a large effect size  and for country of residence, F  = 5.79,p < .001, with a small effect size   .Table5.p < .001). Mean total out-of-homeparticipation was higher among participants living in Sweden compared to thoseliving in Canada (p < .001) and the UK (p< .01) (p <.001).Pairwise comparisons indicated that the mean total out-of-home participation wassignificantly higher for participants living without dementia than those livingwith dementia (p< .01) . PairwisThe older adult study participants living with and without dementia participated inactivities and places outside the home which can be seen as a way of enacting theirsocial citizenship. However, the data from this study support our hypothesis (a)that older adults living with dementia participated in fewer activities and placesoutside the home than the older adults living without dementia.The close examination of the older adults’ participation in domains and places of theACT-OUT questionnaire in this study contributes increased understanding of olderadults’ social citizenship by being engaged in the community. The pattern ofsignificantly lower out-of-home participation among older adults living withdementia compared to those living without dementia was evident in the followingplace domains: Consumer, administration, and self-care places; Social, spiritual andcultural places; and Places for recreation and physical activities. However, therewas no statistically significant difference in participation in Places for medicalcare between the sub-samples. Participation in Places for medical care is importantfor older adults, particularly those living with a diagnosis of dementia .HoweverRegarding aspects of stability and change in out-of-home participation, the resultssupport our hypothesis (b) that the older adults living with dementia abandoned ahigher number of activities and places outside the home between the past and presentthan the older adults without dementia. Despite the decreases in out-of-homeparticipation, both older adults living with and without dementia continued toparticipate in a variety of places. For older adults living with dementia, theirexperiences of socially rich “third places” in the community decline more over timethan other older adults, which can exacerbate negative health effects due to lesssocial stimulation . “Third The finding that older adults living with dementia retained some place types andabandoned others underscores the need to conduct in-depth research on thesocio-environmental characteristics and processes of engagement in specific placetypes. The 10 place types at greater risk of being lost among older adults living withdementia point to the importance for developing responsive policy, services andprograms for social practices, built environmental features, and organizationalcommitment in these place types. Increased knowledge is needed about the nature ofexperience of those activities and places where there was lower participation amongthe older adults living with dementia, to explore the meaning of lower out-of-homeparticipation in relation to social citizenship and to promote cohesion betweenperson and place through age-friendly communities in general and dementia-friendlycommunities specifically . To faciWe can also accept our hypothesis (c) that having a diagnosis of dementia and thecountry of residence have a significant effect on out-of-home participation, whencontrolling for age among our sample. The statistically significant interactioneffect suggests that the experience of living with dementia may vary betweencountries and reinforces the call for research to focus on other aspects ofparticipation, such as contextual consideration, in relation to the older adults’diagnosis of dementia . More spThe mean total out-of-home participation was significantly higher among participantsaged 65–74 years than participants in the oldest age group , andthis corroborates earlier research linking increased age with decreasedparticipation in activities among older adults . HoweverThe interpretation and contribution of this study’s results should be consideredaccording to the following limitations. Due to the cross-sectional study design,it was possible to identify associations but not causal relationships among thestudy factors. Each participant interpreted past participation individually andretrospectively. Thus, the heterogeneity of responses is a limiting factor forcomparisons across individual participants. The relatively small sample sizerecruited using purposive sampling was sufficient to meet the assumptions of thestatistical models; however, it may limit the generalizability of the resultsparticularly due to contextual differences between countries . The resThis study has proposed a strengths-based view of the older adults living with andwithout dementia who enacted their social citizenship through participation inactivities and places in their communities. The results underline the importance toconsider not only older adults’ diagnosis of dementia, but also structural andcontextual aspects of their country of residence which may also significantly impactout-of-home participation. More specifically, to enable the social citizenship ofolder adults, it is important to closely examine the types of activities and placeswhere older adults living with dementia had significantly lower participation thanolder adults living without dementia, and to identify implications for responsiveprograms, policies and built environmental interventions.


Dual-task walking is a good paradigm to measure the walking ability of stroke patients in daily life. It allows for a better observation of brain activation under dual-task walking to assess the impact of the different tasks on the patient when combining with functional near-infrared spectroscopy (fNIRS). This review aims to summarize the cortical change of the prefrontal cortex (PFC) detected in single-task and dual-task walking in stroke patients.Six databases  were systematically searched for relevant studies, from inception to August 2022. Studies that measured the brain activation of single-task and dual-task walking in stroke patients were included. The main outcome of the study was PFC activity measured using fNIRS. In addition, a subgroup analysis was also performed for study characteristics based on HbO to analyze the different effects of disease duration and the type of dual task.SMD = 0.340, P = 0.02, I2 = 7.853%, 95% CI = 0.054–0.626). The secondary analysis showed a significant difference in PFC activation when performing dual-task walking and single-task walking in chronic patients , but not in subacute patients . In addition, performing walking combining serial subtraction , obstacle crossing , or a verbal task  had more PFC activation than single-task walking, while performing the n-back task did not show significant differentiation .Ten articles were included in the final review, and nine articles were included in the quantitative meta-analysis. The primary analysis showed more significant PFC activation in stroke patients performing dual-task walking than single-task walking (Different dual-task paradigms produce different levels of dual-task interference in stroke patients with different disease durations, and it is important to choose the matching dual-task type in relation to the walking ability and cognitive ability of the patient, in order to better improve the assessment and training effects.https://www.crd.york.ac.uk/prospero/, identifier: CRD42022356699. Stroke is the second leading cause of death and disability worldwide  walking is a good and valuable paradigm for exploring the walking abilities of stroke patients in daily life  processing is not affected  guidelines  Population: adult participants with stroke. (2) Intervention: Participants in the studies performing dual-task walking . (3) Comparison: The control group was a single walking task. Other tasks were not mixed during the walking. (4) Outcome: Studies used fNIRS to quantify the concentration changes of oxygenated hemoglobin (HbO) in PFC during single-task or dual-task walking. (5) Studies: The study design and the time of publication were not limited. The following types of articles were excluded: (1) conference proceedings, (2) review articles, (3) case reports, (4) retrospective studies, or (5) not written in English.Keyword searches were performed in Medline, Embase, PubMed, Web of Science, CINAHL, and Cochrane Library from inception to August 2022. The search algorithm included all possible combinations of keywords from the following three groups: (1) stroke; (2) dual task, walking, gait, locomotion, mobility, ambulation, lower limb movement, lower limb motor; and (3) fNIRS, functional near infrared spectroscopy, functional near-infrared spectroscopy, NIRS, near-infrared spectroscopy, near infrared spectroscopy. Keywords and medical subject headings (MeSH) terms were used in the search. Relevant professional journals were searched manually when necessary. The specific search algorithm for each database is provided in A standardized data extraction form was used to collect the following methodological and outcome variables from each included study: author(s), year of publication, study design, type of pathology, sample size, participant characteristics , type of dual-task, and fNIRS outcome. Single-task walking was defined as the control group, while dual-task walking was defined as the experimental group. The subacute phase is defined as the range between 7 and 180 days after initial stroke. Chronic stroke is defined as the open-ended time period starting 180 days after initial stroke . For each trial in the articles, we used Comprehensive Meta-Analysis (CMA) software version 2.0  to synthesize an effect size. The overall effect of group comparisons was assessed using the standardized difference in means (SMD) and 95% confidence intervals (CI).I2 index was used to assess the study heterogeneity. In addition, we performed subgroup analyses for study characteristics  to complete secondary analyses on the impacts of different factors. Publication bias was assessed using a visual inspection of funnel plots. Studies that fell outside the funnel shape had a high risk of bias.The effect sizes were interpreted as 0.2 for a small effect, 0.5 for a moderate effect, and 0.8 or greater for a large effect  Research Quality Assessment tool was used to assess the quality of each included study by two reviewers (QL and WJ). The “Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies” was used for cross-sectional studies. The “Quality Assessment of Controlled Intervention Studies” was used for RCT. Each tool consisted of 14 questions. The study quality assessment helped to measure the strength of scientific evidence, but it was not used to determine the inclusion of studies. The GRADEPro  was used for assessing the quality of the evidence. When two reviewers had a disagreement, a third person (SX) was consulted to resolve it.A total of 377 articles were retrieved in the database from six databases, and three articles were identified through other sources. After removing duplicates, 189 eligible records were retrieved. In total, 174 articles were excluded from the title and abstract screening. After full-text reading, five articles were excluded because they did not meet the criteria. Finally, 10 articles were included in the review, and nine articles were included in the meta-quantitative analysis. One paper was excluded from the meta-analysis because it only performed the difference value between HbO and HbR without reporting the HbO data separately. The detailed process is shown in SMD = 0.340, P = 0.02, I2 = 7.853%, 95% CI = 0.054–0.626).SMD = 0.369, P = 0.038, I2 = 13.692%, 95%CI = 0.020–0.717), while no significant ST-DT diff was found in patients with subacute stroke .Subgroup analyses according to disease duration are shown in SMD = 0.516, P < 0.001, I2 = 0%, 95% CI = 0.239–0.794), obstacle crossing , or verbal task  had greater PFC activation than performing single-task walking. Combined with the n-back task, it did not show a significant differentiation of activation compared with single-task walking .Subgroup analyses according to the type of dual-task are shown in I2 of the combined results decreased to 0. We considered this study to have a large heterogeneity due to differences in design and equipment. Our results were robust when we included other studies with less heterogeneity.To further explore the effect of each article on the total effect size and the accuracy of the results, we performed a sensitivity analysis by sequentially excluding one of the studies, and then repeating the analysis. The results are shown in This study systematically reviews and quantifies the differences in PFC activation between stroke patients performing single-task walking and dual-task walking. Our meta-analysis shows that PFC activation is higher in stroke patients performing dual-task walking than single-task walking. This differences in this PFC activation also vary according to the different types of dual-task and different disease durations in stroke groups.Our meta-analysis showed that there was a significant difference in PFC activation between stroke patients performing single-task walking and dual-task walking, which was mainly manifested by an activation degree that was significantly increased during dual-task walking. The following are possible explanations for the results. First, compared with single-task processing, dual-task processing requires additional executive functions to resolve the interference between tasks. Previous studies indicated that the PFC is the main brain region responsible for executive functions in dual-task processing. The PFC has three subregions. The frontopolar cortex is mainly related to coordinating independent tasks processing. The medial frontal cortex forms reward expectations for task setting, based on motivational cues. The lateral prefrontal cortex is mainly responsible for the selection and characterization of task setting rules (Koechlin et al., However, a study showed the opposite result. Collett et al. showed that dual task brought lower PFC activity than single task (Collett et al., In our meta-analysis, chronic patients showed small effect sizes, but there were no significant effect sizes in patients in the subacute phase. We interpret that the source of the difference is existence of a “ceiling” phenomenon and can be explained by models of limited capacity. Additional cognitive tasks make it difficult to activate PFC more when the patients already perform single task at or near maximum prefrontal replenishment capacity. This phenomenon has been found in multiple studies (Chatterjee et al., Another finding of our study is the different ST-DT diff when stroke patients perform different dual-task types. In our study, serial subtraction, crossing obstacles, and verbal dual tasks all showed moderate effect sizes. The largest effect size of ST-DT diff was found in the verbal task. This is consistent with the results reported by Bishnoi et al. . Verbal The n-back task while walking did not show significant ST-DT diff, which is consistent with the results reported by Hermand et al. . This phIt was previously thought that the unique features of the dual-task were allowed to assess the risk of falling and the potential of patients to return to their homes and communities (Hyndman and Ashburn, The result may also inform the creation of future intervention programs for the rehabilitation of stroke patients. Many current dual-task trainings aim to use the dual-task practice to improve walking ability and executive function in stroke patients (Liu et al., On the other hand, we do not recommend starting dual-task training in subacute patients, which not only increases the risk during training, but also, the training effect may not meet expectations. One study found that dual-task walking speed increased after dual-task training in patients with better walking abilities, but this was not effective in those with poorer abilities (Collett et al., Our study also had certain limitations. Not only did stroke patients have activity changes in PFC during the performance of dual-task, but other brain regions such as SMA and PMC also underwent characteristic and regular changes in response to increased cognitive tasks. However, these messages were not included in our review, due to the fewer studies analyzing the activations in these brain regions. Additionally, the number of included studies in each of our subgroups varied, which may have biased the review. For example, when we compared the activation differences between subacute and chronic patients, the types of dual-task included in two subgroups included were different. Secondly, we found that most of the included studies used the combined HbO values of the left and right PFC, or compared the difference between the left and right hemispheres. Differences in PFC between the healthy and affected sides were less frequently reported. In general, strokes mostly involve unilateral damage. It is inconclusive as to whether this neural tissue damage makes the results of dual-task response different on the affected side of the brain. Moreover, the number of studies and the limited information available made it difficult to analyze other confounding factors, such as ischemic stroke vs. hemorrhagic stroke, different age groups of the stroke population, and treadmill vs. ground walking. The impacts of these factors should continue to be explored in the future.Our meta-analysis shows differences in PFC activation after stroke between the performance of single and dual tasks, based on fNIRS measurements. It varies with the task type and disease duration. The results provide informative suggestions for the future clinical use of the dual-task paradigm to assess the extent of patient walking recovery and training effectiveness, and to predict community walking ability. This also provides a theoretical basis for a better understanding of post-stroke walking behavior deficits and the development of strategies to optimize safe mobility after stroke.The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.QW, SX, WD, and CG chose the topic. QW, SZ, and YSu searched and assessed the studies. WD and SX extracted and analyzed the data. QW, WD, YSh, and CK participated in the writing process. TW, CG, and YZ supervised in the whole process and made the final decision. All authors listed have made a substantial and direct contribution to the work.


Multi-lead pacing is a potential therapy to improve response to cardiac resynchronization therapy (CRT) by providing rapid activation of the myocardium from multiple sites. Here, we perform a meta-analysis of randomized controlled trials to assess the efficacy of multi-lead pacing.P = 0.93] or LV ejection fraction . There was a borderline significant improvement in Minnesota Living With Heart Failure Questionnaire score for multi-lead pacing vs. conventional CRT , but the difference was not significant when only patients receiving LV-only multi-lead pacing were included . There was also no difference between groups for 6-min walk test  or New York Heart Association class at follow-up . There was no difference in mortality between groups .A literature search was performed which identified 251 unique records. After screening, 6 studies were found to meet inclusion criteria, with 415 patients included in the meta-analysis. Four studies performed multi-lead pacing with two left ventricular (LV) leads and one right ventricular (RV) lead. One study used two RV leads and one LV lead, and one study used both configurations. There was no difference between multi-lead pacing and conventional CRT in LV end-systolic volume [mean difference (MD) −0.54 mL, This meta-analysis does not support the use of multi-lead pacing for CRT delivery. However, significant variation between studies was noted, and therefore a benefit for multi-lead pacing in select patients cannot be excluded, and further investigation may be warranted.  Cardiac resynchronization therapy (CRT) is a well-established, effective therapy for patients with dyssynchronous heart failure, however, 30% of patients fail to improve after implantation.3-trialMulti-lead pacing is an alternative to multi-point pacing, and theoretically allows the recruitment of a larger volume of myocardium, as greater separation between pacing electrodes can be achieved.The systematic review and meta-analysis was performed in accordance with the PRISMA statement.Data from included studies were extracted by two reviewers independently (M.K.E. and N.W.). Data recorded included trial design, number of patients, inclusion and exclusion criteria, baseline patient characteristics, and outcomes of pre-specified efficacy endpoints. The potential for bias for each eligible study was assessed using version 2 of the Cochrane Risk of Bias Tool (RoB) by two reviewers independently (M.K.E. and N.W.). Risk of bias was assessed separately for echocardiographic and symptomatic endpoints.Stata Statistical Software: Release 16. College Station, TX, USA: StataCorp LLC) using the ‘meta’ command. Mean difference (MD) with 95% confidence intervals (CIs) were computed for continuous variables. Outcome data expressed as improvement from baseline was used preferentially if available but studies that reported absolute values at follow-up only (including cross-over studies) were still included and outcome data were combined using unstandardized MD with 95% CIs.P < 0.1, and I2 where heterogeneity was considered low, moderate, and high for I2 values of <30%, 30–60% and >60%, respectively.P-value <0.05 for the MD or OR of efficacy endpoints was considered significant.Statistical analyses and creation of forest plots was performed using the Stata 16 software package , which was included in the meta-analysis. The study also reported further outcome data for a smaller subset of patients at 12 months (n = 40), which was not included. The full results of the TRUST-CRT trial as described in the protocol have not been published251 unique records were identified in the initial search, of which 224 were excluded after initial title/abstract screen. Twenty-seven full text articles were assessed for eligibility and six were found to meet inclusion criteria and were included in the quantitative synthesis . For LV-only multi-lead pacing (two LV leads and one RV lead), there was also no difference in LVESV  with low heterogeneity between studies . Similarly, there was no difference in LV ejection fraction between groups for both pacing configurations  and for LV-only multi-lead pacing ; however, heterogeneity between studies was high .Five studies reported echocardiographic outcomes , P = 0.27], with a moderate degree of heterogeneity between studies . When both multi-lead pacing configurations were included, there was a lower symptom burden measured via MLWHF questionnaire for multi-lead pacing compared to conventional CRT and this reached borderline statistical significance  with low heterogeneity between studies . However, there was no significant difference for LV-only multi-lead pacing , with heterogeneity between studies also low . There was no difference between groups in the proportion of patients who were NYHA class III or IV at follow-up for both lead configurations  and for LV-only multi-lead pacing , though heterogeneity between studies was moderate  and high , respectively.Five studies reported symptomatic outcomes using 6-min walk test (6MWT), the Minnesota Living with Heart Failure Questionnaire (MLWHF) and NYHA class , P = 0.71]. Heterogeneity was low between studies for both analyses .Mortality outcomes were reported for the four parallel RCTs ,3 trialWe report the largest meta-analysis of RCTs on the use of multi-lead pacing for CRT to date. We found no significant difference in LV ejection fraction or end-systolic volumes at follow-up between multi-lead pacing or conventional biventricular CRT. There was a borderline-significant improvement in patient-reported symptoms via the MLWHF questionnaire in the multi-lead pacing group compared to conventional CRT, however there was no difference when only patients who received LV-only multi-lead pacing were included. Moreover, only two of the studies .There are significant drawbacks to a multi-lead pacing approach. Placing an additional lead, particularly within the coronary sinus, can be technically challenging, and inevitably increases procedural and fluoroscopy times. Multi-site pacing is associated with higher rates of battery depletion,,et al.,et al.There is evidence from mechanistic and animal model studies that the benefits of multi-lead pacing may be restricted to patients who do not achieve an optimal response from conventional CRT with a single LV lead. In an invasive haemodynamic study of 16 patients, only acute non-responders had an incremental benefit with multi-lead pacing over conventional CRT.,et al.et al.The studies included in this meta-analysis were very heterogeneous in terms of the patient cohort, study design and pacing lead configuration. The inclusion criteria varied markedly between studies. The TRIP-HF trial only included patients with permanent atrial fibrillation (AF) who required pacing and had severe LV systolic impairment.3 trial3 study only included non-responders to CRT, defined as unchanged or worsened clinical composite score at 6 months.The V,,,et al.,et al.et al. did demonstrate some evidence for a beneficial effect of their multi-lead pacing configuration, with a higher proportion of echocardiographic responders in a subset of patients who had extended follow-up, there was no benefit observed in the study by Rodgers et al. for multi-lead pacing in Group B patient (two RV leads + one LV lead), with the overall study findings being driven by the benefit observed in Group A (two LV leads + one RV lead).The pacing configuration also varied between groups. As previous discussed, while the majority of trials performed multi-lead pacing using two LV leads and one RV lead,,The main limitations of the study are related to the variability of the studies included. While 415 patients were included in the study, the cohorts of patients in each trial differed. Therefore, it is difficult to generalize the findings of this meta-analysis to ‘all-comers’ who are indicated for CRT. Furthermore, while no overall benefit was found for multi-lead pacing when the studies were combined, it is possible that the additional of a third ventricular lead may be beneficial in specific subgroups of patients. For example, patients with permanent AF who require pacing for bradycardia did appear to benefit from multi-lead pacing in the TRIP-HF trial.This meta-analysis does not demonstrate significant benefits for multi-lead pacing over conventional CRT. However, significant heterogeneity between studies in terms of inclusion criteria, trial design and multi-lead pacing configuration were noted, and it remains possible that multi-lead pacing is beneficial in selective patients. Further investigation may be warranted in sub-groups of patients undergoing CRT, such as those with atrial fibrillation or ischaemic cardiomyopathy.The data underlying this article are available in the article and in its online supplementary material.Mark K. Elliott is a cardiology trainee in the North East Thames deanery and a PhD research fellow working in the school of Biomedical Engineering and Imaging Sciences at King’s College London. His research interests include cardiac resynchronization therapy, endocardial left ventricular pacing, conduction system pacing, and electrocardiographic imaging.The department is supported by the Wellcome/EP SRC Centre for Medical Engineering (WT203148/Z/16/Z).Conflict of interest: The department is supported by the Wellcome/EPSRC Centre for Medical Engineering (WT203148/Z/16/Z). Outside of the submitted work, B.S.S. is supported by a project grant from NIHR and has received speaker fees from EBR systems. M.K.E., V.M., and J.G. have received fellowship funding from Abbott. S.N. acknowledges support from the UK Engineering and Physical Sciences Research Council , the British Heart Foundation , US National Institutes of Health (NIH R01-HL152256), European Research Council (ERC PREDICT-HF 864055), and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. C.A.R. receives research funding and/or consultation fees from Abbott, Medtronic, Boston Scientific, Spectranetics and MicroPort outside of the submitted work.


IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as  With recent advancements in omics study, microarray technology is being increasingly used to uncover the underlying mechanisms of mental illness , 2. MajoMeanwhile, the advantage of integrating expression-profiling data according to the source of different materials allows for precise isolation of tissue-enriched or tissue-specific genes or pathways that contributed to mental illness . The humMajor depressive disorder and BP exhibit similar severe depressive symptoms and show less difference in the duration of affective episodes during the course of illness . These tAll the transcriptomic datasets by the platform of mRNA microarray expression profiling on MDD or BP were retrieved using a comprehensive query of Gene Expression Omnibus (GEO). We screened 36 datasets by the following conditions: (1) The datasets included both healthy participants and patients; (2) the datasets containing other factors such as smoking and medication were removed. All demographic information of each participant, which contained age, sex, etc., was extracted from the selected datasets . SamplesLimma package (v.3.36) was used to process microarray data for an individual dataset, the differences between patients with MDD or BP and the control group were assessed using a multivariate linear model . DEGs were then analyzed for subsequent integration analysis of the entire datasets and construction of co-expression networks. Meanwhile, to exclude the false positive possibility caused by confounding factors including age, sex, PH, etc., a t-test analysis between gene expression and demographic status of individual participants was performed, and confounding factors genes were removed while looking for tissue-specific genes  have been produced. One study has reported that ComBat is superior to other methods in terms of precision, accuracy, and overall performance , thereby providing appropriate soft-threshold power for network construction .Weighted gene co-expression network analysis was used to construct gene co-expression networks for large scale gene expression profiling from various tissues . Construtruction . Converttruction . In the clusterProfiler was used to evaluate the biological pathways and processes of tissue-specific modules related to diseases  significantly associated with MDD or BP, a comprehensive query was made using the GWAS Catalog Database of NHGRI-EBI, which contains GWAS analysis documents and SNP loci. Studies from which variants were collected are determined according to the following conditions: (1) Studies must focus on either MDD or BP; (2) Studies that have indirect phenotypic characteristics, such as obesity and education level, were excluded . FinallyA total of 22 datasets covering 1,023 participants with MDD and 14 datasets covering 697 participants with BP were downloaded from the repository, and we established strategies for identifying tissue-specific modules, annotated pathways to elucidate tissue biological function, and tissue-specific genes through integrated analysis of those datasets . Here, wn = 7,678), while HPC tissues contained the lowest (n = 674). The two brain areas PFC and ACC shared the highest intersection of DEG numbers in different tissues , followed by that between PFC and PBMC . For BP, PFC tissues contained the highest DEG numbers , while HPC contained the lowest (n = 938). PFC and WB showed the highest intersection of DEGs , followed by those in PFC and PBMC .The number of DEGs in different tissues for the MDD- or BP- are shown in Then, expression matrices of these identified DEGs were extracted from different tissues of patients (only used case samples) with MDD- and BP-, including 481 and 295 samples, respectively . The hiep-value < 0.05.A total of 11,997 DEGs from the MDD tissues and 11,025 DEGs from BP tissues were combined into an overall expression matrix as input data. Using WGCNA, 17 and 18 co-expression patterns (modules) were assigned to the MDD- and BP- datasets, respectively. Correlations between each module and each tissue were evaluated to identify tissue-specific modules, for which the threshold was set as an absolute value of correlation coefficient | r| > 0.6 and r = 0.6)], CRE ; PFC [bisque4 module (r = 0.8) and lightcoral module (r = –0.94)]; PBMC [lightskyblue module (r = 0.85) and darkolivegreen module (r = –0.96)]; and WB [lightgreen module (r = 0.98) and saddlebrown module (r = –0.91)]. For BP-, six tissue-specific modules were found in the tissues: CRE [darkgreen module (r = 0.92) and greenyellow module (r = –0.95)], PFC (pink module (r = 0.93) and brown module (r = –0.94)], and PBMC [yellow module (r = 0.83) and blue module (r = –0.98)].In MDD-, nine tissue-specific modules across different tissues were identified and distributed across the 4 tissues, including ACC [darkslateblue module  from the 7 tissue samples shared by MDD and BP (except AMY) and constructed a co-expression network module based on the matrix of these genes. After parameter optimization, it is ensured that the characteristic genes of each module have the same distribution in MDD-BP. The two diseases of BP and MDD are analyzed for the correlation of the organization, and then the results of the two correlations analysis are merged. We focus on the modules with the consistency of positive and negative correlations in the two diseases and set N/A for the inconsistent modules , 4A.r = 0.67) was mainly related to metabolism and neuronal processes, such as carbon metabolism and neuroactive ligand-receptor interaction, while the magenta module in PFC (r = –0.93) was mainly involved in cortipair synthesis and secretion, glycosphingolipid biosynthesis-lacto and neolacto series, and morphine addiction. The cyan module in PBMC (r = –0.98) was correlated with amino-acid metabolism-related pathways  was collected using PFC obtained from patients with MDD and BP and healthy controls. As shown in SST and CRHBP were significantly downregulated in PFC obtained from patients with MDD and BP. Based on the results of the WGCNA analysis, it can be concluded that SST and CRHBP may be candidate marker genes of MDD and BP.In MDD-BP, we found the tissue-specific gene Tissue-specific genes are a class of genes that are highly expressed in specific tissues and play a role in transcriptional regulation, development, stress response, and even disease development –36. TherWeighted gene co-expression network analysis is essential to elucidate co-expression patterns or hub genes from genome-wide expression profiling , 42. MosWe emphasized the need to focus on tissue-specific modules, the brain tissue-specific modules were analyzed to be at a significant level for the association between co-expression genes and tissues, which also reflect the biological functions of the differential genes specifically enriched into a particular tissue of patients who are depressed. In the study of MDD and BP, blood and brain tissue samples are always the main sample sources. We have included both kinds of tissues in our research to compare their similar yet different aspects in the development of diseases from an overall perspective. Our results showed that MDD and BP blood samples contained several tissue-specific modules, including epigenes enriched in immune and inflammatory pathways, such as the Th1 and Th2 cell differentiation pathway and human T-cell leukemia virus 1 infection pathway. Immune and inflammatory pathways have been detected in MDD and BP blood samples using single-disease analysis, indicating that the two diseases are likely caused by an immune disorder affecting the function of the CNS –45. PrecIGFBP2, a tissue-specific gene in PFC tissues, mainly responded to glucocorticoids and corticosteroids, appear to play a governing role in insulin-like growth factor (IGF) regulation in the central nervous system. This protein in the alterations in neurodevelopment and neuroprotection has been observed in mood disorder by another study , is suggested to be involved in the pathogenesis of MDD and the antidepressant treatment response   . It has ic value –58. Our ignaling –61. ADCYactivity , 31, 62.SST and CRHBP were shared in both MDD and BP; this finding was validated by independent studies on MDD and BP, which are detailed in what follows. Studies examining patients with MDD and human postmortem brain tissues have shown that somatostatin, encoded by SST, significantly decreases in the dorsolateral PFC (dlPFC), subgenual anterior cingulate cortex (sgACC), and the AMY  can be found in the article/GZ, QW, and CD designed the project. MH and LY performed the WGCNA and integrated analysis. YH provided the view of batch effect removal. MH and LY wrote the first draft of the manuscript. GZ, QW, CD, and GW checked and edited the manuscript. All authors read and approved the final version of the manuscript.


Data on the association of Alternative Healthy Eating Index-2010 (AHEI-2010) with sarcopenia are scarce. We aimed to evaluate the association between adherence to AHEI-2010 and sarcopenia and its components including low muscle mass, low muscle strength, and low muscle performance among elderly people.In this cross-sectional study, which was conducted on 300 older people (150 men and 150 women) aged ≥55 years. Dietary information was done using a valid 117-item food frequency questionnaire (FFQ). To construct AHEI-2010 score, earlier studies were used. Sarcopenia and its components were described based on both former and new European Working Group on Sarcopenia in Older People (EWGSOP) guidelines.We found no significant association between AHEI-2010 score and odds of EWGSOP2-sarcopenia, either before  or after  adjustment for confounders. In gender-stratified analyses, we found a significant protective association between adherence to the AHEI-2010 score and odds of EWGSOP2-sarcopenia among women after controlling for confounders .In conclusion, healthy eating was inversely associated with odds of sarcopenia among women, but not in men. Further studies with a large sample size and prospective design are needed to examine this association. Sarcopenia is a progressive skeletal muscle disorder affecting muscle strength and function , 2. ThisAlong with several non-dietary contributors including telomere length, dietary intake of macro- and micro-nutrients , 7, 8 anThis is particularly important in the Middle East, where the application of other dietary patterns, like Mediterranean diet, might be less relevant. In addition, the growing number of elderly people in this region highlights the need for finding preventive measures, including dietary measures, for sarcopenia , 22. GivThe present work was done based on the dataset of a previous population-based, cross-sectional study conducted from May to October 2011 in Tehran, Iran. Several analyses from that dataset has earlier been published –25, in wA pretested valid food frequency questionnaire (FFQ), containing 117 food items, was used to assess usual dietary intake of participants , 27. In To assess the adherence to the healthy eating guidelines, AHEI-2010 can be a good option . AHEI-202). An DXA scanner (Discovery W S/N 84430) was used to calculate ASM. Based on EWGSOP, low muscle mass was determined as the quantity of muscle mass less than 5.45 kg/m2 and 7.26 kg/m2 for women and men, respectively . When the analysis was controlled for potential confounders, this association remained non-significant . Even further adjustment for BMI did not alter the associations . The same findings were observed in the sex-stratified analyses either before  or after controlling for BMI . The same findings were seen when we applied EWGSOP-sarcopenia definition, either before  or after adjusting for potential confounders  or BMI . In the gender-stratified analyses, no significant relationship was found among men, either before  or after controlling for BMI . However, women in the top tertile of AHEI-2010 score, were 80% less likely to have EWGSOP-sarcopenia compared to those in the bottom tertile before considering BMI . Additional adjustment for BMI made this association non-significant .Crude and multivariable-adjusted ORs for sarcopenia across tertiles of AHEI-2010 score are provided in Table 4. Both in crude and in adjusted models, we failed to find a significant association between adherence to AHEI-2010 and muscle mass, hand grip strength and gait speed.Regression coefficients for the association between AHEI-2010 score and components of sarcopenia are presented in Table 5Table In this cross-sectional study, we failed to find a significant association between adherence to AHEI-2010 and odds of sarcopenia in older adults, even after adjustment for potential confounders. Following sex-stratified analysis, we found a significant relationship between AHEI-2010 and odds of EWGSOP-sarcopenia among women, such that; women in the top tertile of AHEI-2010 score have lower risk of EWGSOP-sarcopenia compared to those in the bottom tertile. To the best of our knowledge, this is the first study investigating the association between AHEI-2010 and sarcopenia.Sarcopenia is linked to adverse health related outcomes, including fall, fracture and mortality , 4. SarcThe gender-discrepant findings in the current study may be related to differences in physical activity levels, testosterone and body fat levels between men and women , 40. HowP = 0.02). This might also affect our findings.Although the exact mechanisms through which healthy diets might affect the risk of sarcopenia is poorly understood, there are some hypotheses in this regard. First is lowered levels of oxidative stress through consumption of these diets , 43. OxiThis study has several major strengths. Although the links between healthy eating and sarcopenia were investigated previously, to our knowledge, this is the first study evaluating the association between AHEI-2010 and odds of sarcopenia and its components. Several potential confounders were adjusted in the current analysis. Additionally, a validated FFQ was used to evaluate dietary intakes. However, some limitations should also be considered. Causality cannot be inferred due to cross-sectional design. Some components of AHEI-2010 such as trans-fats and alcohol consumption were not included in the AHEI-2010 score due to lack of data. Also, the index does not evaluate animal protein sources (such as fish) except red meat. Furthermore, dairy consumption was not included in the scoring method of AHEI-2010, which might further affect the associations. The study was performed on a small sample from a limited area where the DEXA device was located, because of financial limitations and insufficient access to the DEXA device in Tehran (maximum 300 cases). Accordingly, the generalizability of these results should be done cautiously. It should be kept in mind that our study population were relatively young (300 subjects aged > 55 years old) which might further explain lack of finding a significant association.In conclusion, findings from this cross-sectional study revealed that no significant association between adherence to AHEI-2010 and risk of sarcopenia, even after adjustment for potential confounders. However, in the gender-stratified analysis, we found a significant inverse relationship between AHEI-2010 and odds of sarcopenia among women. Further prospective studies are required to confirm the results of present study.


Space-Air-Ground integrated Mobile CrowdSensing (SAG-MCS) problem. Based on multi-source observations from embedded sensors and satellites, an aerial UAV swarm is required to carry out energy-efficient data collection and recharging tasks. Up to date, few studies have explored such multi-task MCS problem with the cooperation of UAV swarm and satellites. To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). Our ms-SDRGN approach incorporates a multi-scale convolutional encoder to process multi-source raw observations for better feature exploitation. We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance. In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. Specifically, we design a heuristic reward function to encourage the agents to achieve global cooperation under partial observability. We train the model to convergence and conduct a series of case studies. Evaluation results show statistical significance and that ms-SDRGN outperforms three state-of-the-art DRL baselines in SAG-MCS. Compared with the best-performing baseline, ms-SDRGN improves 29.0% reward and 3.8% CFE score. We also investigate the scalability and robustness of ms-SDRGN towards DRL environments with diverse observation scales or demanding communication conditions.Mobile crowdsensing (MCS) is attracting considerable attention in the past few years as a new paradigm for large-scale information sensing. Unmanned aerial vehicles (UAVs) have played a significant role in MCS tasks and served as crucial nodes in the newly-proposed space-air-ground integrated network (SAGIN). In this paper, we incorporate SAGIN into MCS task and present a  In the past few years, Mobile Crowdsensing MCS ,2) has r has r2])With the help of high-precision embedded sensors and path planning algorithms , smart uAs the rapid developments and applications of modern network technologies ,19, seveSpace-Air-Ground integrated Mobile CrowdSensing (SAG-MCS). In SAG-MCS scenario, a UAV swarm is used to cooperate autonomously and fly above an area with multiple Points of Interest (PoIs) for coverage and sensing. As illustrated in In this paper, we employ the concept of SAGIN into the data collection task, and present a new MCS framework with a collection of UAVs, ground nodes and satellites, namely On the whole, this paper endeavours to propose a decision-making model for UAVs, which are powered by limited onboard batteries and distributed charging stations, to energy-efficiently and persistently sense and collect PoIs on the ground. The multi-UAV swarm shall perform actions according to local airborne observations and global observations from satellites. The overall optimization objective of the UAV swarm is to maximize the data coverage and geographical fairness among all PoIs, and minimize the power consumed during flying or battery charging.For such an MCS task that has multiple complex objectives, existing approaches that modeling MCS as an optimization problem is no longer effective. However, recently well-explored Deep Reinforcement Learning (DRL) could be a feasible solution. It has achieved great performances in several game-playing tasks  or path We design a realistic SAG-MCS environment with obstacles and charging stations for simulation. To further enhance exploration of the global area, the UAV swarm can acquire multi-source observation inputs from embedded sensors and satellites.Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). It integrates a multi-scale convolutional encoder to process different sizes of observations. This method also utilizes graph attention network , gated ork (GAT ), as welAlthough UAV swarm can receive parts of global observations from satellites, SAG-MCS is formulated as a practical partially-observed problem, and UAVs cannot have access to the overall system states during training. Therefore, we propose a heuristic reward function that only utilizes local observed information, but manages to train the UAV swarm to properly act for optimizing several global metrics.We have designed and conducted several simulation case studies to verify the effectiveness of our stochastic MADRL method and reward function. Additionally, we validate the robustness of the trained model and the multi-scale CNN encoder under different communication conditions and at different environment scales.To this end, this paper formulates the problem as a Partially Observable Markov Decision Process (POMDP) and proposes a stochastic MADRL algorithm in SAG-MCS environment, to perform data collection and task allocation simultaneously. The main contributions of this article are summarized as follows:The remaining part of this paper proceeds as follows: In this section, we review the literature related to mobile crowdsensing problem, DRL approaches for multi-agent systems, and the joint studies of these two topics.Threat to validity ,29: For MCS scenarios usually have multiple constraints and objectives. One of the key issues is how to perform task allocation, or how to choose appropriate action strategies for different tasks. The main tasks of SAG-MCS are data collection by covering PoIs and energy management by keeping batteries charged. UAVs need to automatically select action strategies to meet the data collection requirements under the energy-efficiency constraint. Solving such multi-agent task allocation is an NP-hard problem, and the related research is still in a relatively early stage. Feng et al.  utilizedt, the environment is at state i obtains a observation i selects and executes an action i. The policy is expected to maximize the discounted reward In multi-agent systems, Reinforcement Learning (RL) generally targets at problems of agents sequentially interacting with local environment. At timestep Q is the learned network and Currently, DRL methods have achieved state-of-the-art performance in various RL tasks ,25, and d stable . SpecifiCompared with classical heuristic algorithms, agents can learn a strategy more efficiently and independently through DRL algorithms, so as to achieve multiple objectives in the sensing area simultaneously.To date, several studies have investigated the application of DRL algorithms in the UAV Mobile Base Station (MBS) scenario, which is a sub-topic of MCS. In the UAV MBS scenario, a swarm of UAV serve as mobile base stations to provide long-term communication services for ground users. Liu et al.  proposedDifferent from policy gradient methods, Dai et al.  applied On the basis of MBS, Liu et al. ,39 took Compared with the UAV MBS and MCS works mentioned above, this paper proposes a more complicated and promising SAG-MCS scenario, which incorporates global and local observations from space and air, respectively, and encourages UAVs to interact with charging stations as ground nodes. While ,39,40,41In this section, we design a partially observable space-air-ground integrated MCS system, with space-based remote sensing satellites and an aerial UAV swarm jointly performing the MCS task. We define the problem and present the 2D simulation system model specifically. Then, we describe the design of evaluation metrics.As illustrated in U UAV agents deployed in the simulation area, where the UAVs can perform continuous and horizontal flying movements at a fixed altitude. We define k within a UAV’s t. Note that p between adjacent UAV nodes in training and evaluation. As for the energy consumption, we set the onboard battery status Let T timesteps in total. Each UAV’s position is randomly assigned and their batteries are fully-charged in the beginning. At each timestep t, UAV u can obtain local observation from embedded sensors; while every few timesteps, it can obtain fuzzy global observation from the satellite. Using the multi-scale observations u performs an action t as For each simulation episode, the data collection task in SAG-MCS scenario will last for u can obtain the multi-scale observation t from different sources, as introduced in In SAG-MCS, each UAV agent k, otherwise 0. The second channel contains the locations of obstacles relative to the UAV, where we set pixel value 1 for coordinates of obstacles, otherwise 0. The third channel includes the locations of other UAVs within (1) Local observation n timesteps, satellites will capture fuzzy global observation and transmit the information to all UAVs. As shown in u in global map.(2) Global observation u, u.(3) Auxiliary observation thrust at all directions responsively. We choose to discretize the entire 2-dimensional continuous space into eight directions for simplicity, and UAV agents can apply maximum-thrust (denoted as 1.0 unit), half-thrust (0.5 unit), or zero-thrust (0 unit) at any direction. Note that zero-thrust represents hovering in place. Therefore, the action space in SAG-MCS is defined as:The rotor UAVs are capable of applying different As stated in Data Coverage Index, which describes the average data amounts collected by the whole UAV swarm per timestep, as:k was successfully collected from timestep 1 till t. k and K is the number of PoIs.The first metric is Geographical Fairness Index to evaluate the exploration ability of the UAV team, as:We noticed that in some cases, isolated PoIs in rural areas may not be covered even when the data coverage index is quite high; however, isolated or sparse PoIs in remote areas can carry valuable information in certain scenarios such as disaster relief. Considering the comprehensiveness of the data collection task, we propose the second global metric Equation . When alteam, as:ft=∑k=1KwEnergy Consumption Index is used to indicate the energy-saving status of the UAV swarm. In order to further simulate the energy consumed by multi-rotor UAV in reality, we adopt an equation of power on the flight speed [v is the current flying speed. Parameter A, W, b denote UAV’s front facing area, total weight, and width, respectively. For simplicity, we adopt a general UAV model and specific values are omitted in this paper. In timestep t, we assume the consumed energy u is linear to its battery power, as:u in timestep t. Equations PPEnergy Cower, as:etu=e0+ηefinal global metrics, denoted as coverage-fairness-energy score (CFE score) by a DRL policy After a complete simulation episode, we calculate the metrics mentioned above as e et al. , we defiObviously, our objective is to optimize the policy Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). As illustrated in Due to the multi-scale observation space and complicated SAG-MCS task, we propose a heuristic DRL method named Exploiting observations properly is essential for agents to perceive the current state of RL systems and make corresponding actions. Previous DRL methods  apply multi-layer perceptron (MLP) as linear encoders to process raw observations, which is preferred for scenarios with smaller observation dimensions or less information, such as Cooperative Navigation . Howeveru:Our intuition lies that compared with MLP, convolutional neural network (CNN) is more capable of processing data that has spatial information and large receptive fields, such as images. CNN can integrate information from different input channels as well. So we treat the local observation Such multi-scale features can help UAVs better select actions, by taking full account of: (a) the relative position between current UAV and surrounding PoIs, obstacles or other agents; (b) the correlation of current UAV’s remaining battery and the distance to the closest charging station; (c) the distribution of PoIs in the fuzzy global map for better exploration and coverage.i, we denote i as a set i is interconnected with UAV node j. For all UAV node i towards its different neighbors j as i and its neighboring node j is defined as j using softmax function:For the purpose of multi-agents exchanging information through ad-hoc connections in SAG-MCS, we model the UAV swarm as a graph network, where each node is represented as a UAV, and the edges are the communication links of neighboring UAV pairs. For each node ttention , an attej by weighted summation, which is given by:j after one GAT layer. In addition, Then GAT aggregates information from all adjacent nodes As shown in t stored in the memory unit. After adjacent information aggregation and GRU, we apply an affine transformation layer to Additionally, to make full use of temporal information during the interaction with RL environments and improve long-term performance, we integrate a gated recurrent unit (GRU) to memorize temporal features as:Based on the Q-value produced by DRGN, we can learn a deterministic policy, where each Q-value represents a fixed probability of the corresponding action. However, deterministic policies can easily jump into local optimum and lack for exploration in complex, real-world scenarios. Inspired by the maximum entropy RL framework ,51, we uFirstly, we sample previous interaction experiences from the replay buffer as training inputs. The ms-SDRGN learned model infers a set of Q-value from the experiences. Then, we apply temperatured softmax operation to Q-value for getting the action probability:π=f is a customized activation function and The action entropy represents the action uncertainty of policy t, S is the batch size. The Q-value function Following Soft Q-learning , we alsoOt,at)α,Qloss=1SΣIn this section, we design a heuristic reward function to evaluate the result when the UAV swarm conducted action u, we design an individual coverage term u, and u in one-hop connection. They are expected to improve the data coverage index through both individual exploration and swarm cooperation. Let u’s one-hop neighboring nodes. In addition, we set balance coefficients Firstly, we encourage the UAV swarm to collect data as much as possible. Note that PoIs that within UAV’s coverage range u and charging station c. The charge term Secondly, in order to guide UAVs to charging stations when their batteries are low, we propose a charge term u collides with the fixed boundary in our scenario, otherwise put Other factors such as energy consumption and collisions are considered as well. According to Equation , we simpWe integrate local evaluation terms and define the heuristic reward function as:ru=ruselfFor training simplicity, UAV can still operate when its battery has drained, but it cannot get reward from data collection and will get an extra punishment.In this section, we introduce the setup of experiments and performance metrics. Then, we compare our approach with three state-of-the-art DRL baselines. Case studies are performed to analyze the effectiveness, expansibility and robustness of ms-SDRGN.In this section, we use Pytorch 1.9.0 to perform experiments on Ubuntu 20.04 servers with two NVIDIA 3080 GPUs and an A100 GPU. In the SAG-MCS simulation environment, we set the 2D continuous target area of quations .In our implementation, the target entropy factor is set to One simulation episode lasts for 100 timesteps, and each DRL model interacts with the simulation environment for 50,000 episodes in total. Interaction experiences will be pushed to the replay buffer concurrently. After each simulation episode, the learned network is trained for 4 times using the experiences sampled from the replay buffer, while the target network is updated every 5 episodes by directly copying the parameters from the learned network. After training, we test the converged models for 1000 episodes to reduce randomness.As introduced in Episodic Reward: calculates the accumulated reward of the whole evaluation episode. It generally evaluates the SAG-MCS task achievements by the UAV swarm, considering data collection, battery management and collisions.Data Coverage Index (Geographical Fairness Index (Energy Consumption Index (CFE Score (Equation . We expeTo validate the feasibility and effectiveness of our SAG-MCS environment design and the heuristic reward function, we first present the learning curves of episodic reward and the global metrics over time. During the training phase, we evaluate the model for 20 episodes after every 100 training episodes, and calculate the average global metrics and accumulated reward, as illustrated in In Through visualization, we can observe that UAVs have learned to appropriately assign tasks at different remaining batteries. When its battery drops to around 25~40%, the UAV will proceed to the closest charging stations for battery exchange. In each simulation episode with 100 timesteps, the whole swarm rarely runs out of power, as such a charging process will happen two times for each UAV.We then compare our approach ms-SDRGN with three DRL baselines, including DGN , DQN 2424 and MAWe have evaluated the converged methods for 1000 episodes, and taken the mean value and standard deviation of all metrics, as shown in The evaluation results are presented in Firstly, the proposed approach ms-SDRGN outperforms all other baselines in terms of reward and coverage index significantly. It demonstrates that with the help of multi-scale convolutional encoder and graph-based communication, ms-SDRGN achieves better data collection and energy management efficiency in SAG-MCS scenario. Compared with DQN and DGN, ms-SDRGN can better sense the surrounding environment from previous experiences in the memory unit, and make decisions more efficiently between seeking for more PoIs or returning for charging.Secondly, from the perspective of fairness and energy, MAAC improves 0.005 fairness and 0.0075 energy index than ms-SDRGN. As a fully observable algorithm, we believe that MAAC can achieve similar cooperative exploration as ms-SDRGN using the observation embeddings from the whole UAV swarm. Regardless of extracting features from neighboring UAV nodes or from the memory unit, MAAC has a simpler objective to reduce its energy consumption for getting a higher reward.Furthermore, the reward standard deviation of ms-SDRGN is higher than other methods, which may be attributed to randomness generated by the complex MADRL framework.p varies in In practical wireless networking applications, communication losses commonly occur in forms of delay, congestion or packet losses. To better cope with such real-world demanding communication conditions, we assume a p is larger than 0.4. In terms of the major metric CFE score in From Random communication dropout can affect the stability of timing correlation in GRU memory unit. However, after trained in environment with 50% probability of communication losses, our ms-SDRGN is proved to be more robust, and will not result in significant performance loss even under unreliable communication conditions.Next, we proceed to verify the performance of the multi-scale convolutional encoder. In actual MCS tasks, UAVs could encounter various densities of overground PoIs. For regions with dense PoI distributions such as modern cities, we hope to perform finer-grained observations for higher feature resolution. While we can perform coarse-grained or lightsized observations for areas with sparse PoIs. In order to handle tasks of different observation scales and enhance robustness, we implement CNNs as the multi-scale encoder, which technically is more applicable than linear encoders for large-scale observations. Therefore, we expect to compare the front-end multi-scale convolutional encoder with original linear encoder using different local observation scales.In this experiment, we simulate different sizes of observation inputs by proportionally scaling the whole map, which could maintain the distribution of all elements and ensure comparison fairness. Specifically, we set the original environment setting introduced in The evaluation results of four environment scales are presented in Finally, we conduct an ablation study by separately removing components of ms-SDRGN, including multi-scale encoder, GAT layers, and GRU. We evaluate each case for 1000 episodes and the average results are listed in It can be observed in In this section, we discuss two limitations of our method and explore future directions for practical implementation.Firstly, the computational complexity is crucial for practical applications. The proposed MADRL approach functions in a decentralized manner. Each UAV agent infers its action using on-board processor and executes the action subsequently. In addition, the multi-scale convolutional encoder introduced in Secondly, hand-crafted reward function limits the scalability. The heuristic reward function designed in This paper introduced a partially observable MCS scenario named SAG-MCS, with an aerial UAV swarm jointly performing data collection task under energy limits. We proposed a value-based MADRL model named ms-SDRGN to address this multi-agent problem. Conclusively, ms-SDRGN applied a multi-scale convolutional encoder to handle the multi-scale observations, and utilized GAT and GRU for modeling communications and providing long-term memories. Effectively, a maximum-entropy method with configurable action entropy was employed to learn a stochastic policy. Experiments were conducted to demonstrate the superiority of our model compared with other DRL baselines, and validate the necessity of major components in ms-SDRGN. In addition, we analyzed the effectiveness of the communication dropout setting and the front-end CNN encoder. Future works will be focused on implementing fully continuous action space and exploring multi-stage multi-agent scenarios.