Post-traumatic stress disorder (PTSD) is prevalent in women; however, preclinical research on PTSD has predominantly been conducted in male animals. Using a predator scent stress (PSS) rodent model of PTSD, we sought to determine if stress-susceptible female rats show altered monoamine concentrations in brain regions associated with PTSD: the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), and dorsal (dHIPP) and ventral (vHIPP) hippocampus. Female Sprague–Dawley rats were exposed to a single, 10-min PSS exposure and tested for persistent anhedonia, fear, and anxiety-like behavior over four weeks. Rats were phenotyped as stress-Susceptible based on sucrose consumption in the sucrose preference task and time spent in the open arms of the elevated plus maze. Brain tissue was collected, and norepinephrine, dopamine, serotonin, and their metabolites were quantified using high-performance liquid chromatography. Stress-susceptibility in female rats was associated with increased dopamine and serotonin turnover in the mPFC. Susceptibility was also associated with elevated dopamine turnover in the NAc and increased norepinephrine in the vHIPP. Our findings suggest that stress-susceptibility after a single stress exposure is associated with long-term effects on monoamine function in female rats. These data suggest interventions that decrease monoamine turnover, such as MAOIs, may be effective in the treatment of PTSD in women. Post-traumatic stress disorder (PTSD) is a psychiatric disorder characterized by anxiety, anhedonia, and vigilance that persists for months to years after a trauma exposure ,2,3. OnlAccumulating evidence suggests the monoamine system is implicated in PTSD. Clinical Pharmacotherapies targeting serotonin show moderate efficacy in treating symptoms of PTSD, with the selective serotonin reuptake inhibitors (SSRIs) sertraline and paroxetine currently being the only medications approved by the FDA for PTSD . PatientThe neurobiology underlying susceptibility to developing long-term symptoms following a single stressor can be examined using preclinical models, of which there are several . Preclinical models that consider susceptibility and resilience to the long-term effects of a single stress exposure particularly have face validity for the study of PTSD . Two sucBrain monoamine dysregulation underlying PTSD, anxiety, and depression has been investigated at the preclinical level exclusively in male rodents, both using models that consider susceptibility to stress and those that compare the entire population of stressed rodents to unstressed controls. Quantification of brain monoamines via ex vivo tissue punch analysis following footshock, forced swim, social defeat/isolation, predator/predator scent, and chronic/repeated stress find disrupted monoamine system function in the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), and hippocampus (HIPP) associated with stress exposure ,26,27,28The present study aims to test the hypothesis that the persistent anxiety- and anhedonia-like behavior observed in females after PSS is associated with disrupted monoamine concentrations in key brain regions associated with PTSD: mPFC, NAc, and the dorsal (dHIPP) and ventral (vHIPP) hippocampus. We hypothesize that, as in males, stress susceptibility in females will be associated with NE and DA dysregulation, specifically increased NE in the mPFC, dHIPP, and vHIPP and increased DA, DOPAC, and DA turnover in the mPFC; increased DA in the NAc; and decreased HVA and increased DOPAC in the hippocampus. We also expect to observe decreases in the 5-HT metabolite 5-hydroxyindoleacetic acid (5-HIAA) in the mPFC, decreased 5-HT and 5-HT turnover in the mPFC, dHIPP, and vHIPP, and increased 5-HT and 5-HT turnover in the NAc of stress-Susceptible females. Fifty-four female Sprague–Dawley rats arrived at 8 weeks of age and were individually housed in ventilated cages on a reverse 12-h light/dark cycle (lights off at 7 a.m.). Rats habituated to the vivarium for 7 days before the experiment started with access to standard rat chow and water ad libitum. Following stress or control exposures, subjects were food-restricted to 20 g/day. All procedures were authorized and approved by the University of Florida’s Institutional Animal Care & Use Committee.n = 40) or unscented paper . Rats could not interact with the filter paper and new paper was used for each rat. Chambers were cleaned with 70% ethanol between rats. Exposure sessions were recorded to measure time spent freezing. Immediately after TMT/control exposure, vaginal lavage was used to collect samples to determine the estrous cycle phase , as described in Blount et al.  and displayed increased mean startle responses  compared to Controls. TMT-exposed rats also showed lower sucrose preference  and decreased sucrose consumption (mL)  relative to Controls. To determine if TMT altered anxiety-like behavior overall, t-tests were used to compare dependent variables between TMT and Control groups. TMT-exposed rats spent less time in the open arms of the EPM  with Susceptible rats showing less time spent in the OA relative to Control (p = 0.001) and Resilient (p = 0.016) rats. There was also a significant effect of Phenotype on time spent in the closed arms  with Susceptible rats spending more time in the CA compared to Controls (p = 0.006). CA entries and OA entries did not differ between phenotypes. A one-way ANOVA revealed a significant effect of Phenotype on time spent in the open arms of the EPM . Susceptible rats have greater mean startle amplitude than both Control (p < 0.001) and Resilient (p < 0.001) rats.Due to ASR equipment malfunction, data files from two Resilient rats and one Susceptible rat were corrupted and could not be included in analyses. A one-way ANOVA revealed no differences in percent habituation between phenotypes, but a significant difference in mean startle amplitude . Susceptible rats decreased sucrose intake compared to Control (p < 0.001) and Resilient (p < 0.001) rats. No phenotypic differences were detected in sucrose preference; however, there was an effect on sucrose consumed  with no significant multiple comparisons. No significant phenotypic differences were detected in latencies to the light or dark sides of the chamber. A one-way ANOVA revealed significant effects of Phenotype on time spent in the dark chamber of the L-D box task  and multiple comparisons revealed both Susceptible (p = 0.001) and Resilient (p = 0.02) rats decreased HVA relative to Controls  and Resilient (p = 0.01) rats showing increased DA turnover compared to Controls, and Susceptible showing a greater increase than Resilient  and Resilient (p = 0.02) displaying increased DA turnover relative to Controls  and Resilient (p = 0.01) compared to Controls . In the vHIPP, there was a significant effect of Phenotype in DOPAC [F  = 14.15, Controls I. No sigControls J,K. Therparisons L. p = 0.046). Time spent in the OA of the EPM was negatively correlated with DA turnover in the mPFC  and NAc . Sucrose consumed was negatively correlated with DA turnover in the mPFC  and NAc . DA turnover in the NAc was positively correlated with time spent in the dark chamber of the L-D box .DA and DA turnover correlated with several behaviors. mPFC DA was negatively correlated with time spent in the dark chamber of the L-D box  and Resilient (p = 0.03) rats, with no difference between Resilient and Control. No phenotype effects were found in 5-HIAA, 5-HT, or 5-HT turnover in the NAc, dHIPP, or vHIPP  was detected.No significant differences were detected in 5-HIAA levels in the mPFC A There wor vHIPP D–H. A siThe present study examined the effect of a single exposure to PSS on tissue levels of monoamines, their metabolites, and turnover in the mPFC, NAc, dHIPP, and vHIPP of female rats. Heterogenous responses to PSS allowed for the separation of stress-exposed female rats into Susceptible and Resilient phenotypes. Phenotypic patterns of behavior in EPM, sucrose preference, and the light–dark box were accompanied by distinct monoamine changes. Stress-susceptibility was accompanied by reduced DA and HVA and a trend for reduced 5-HT in the mPFC. Susceptible rats showed an elevated turnover of DA and 5-HT within the mPFC and elevation of DA turnover in the NAc. Susceptible rats also exhibited increased vHIPP NE and decreased DOPAC, and an effect of phenotype was observed in vHIPP DA turnover. Though brain monoamine and metabolites were assessed days to weeks after behavioral tasks, decreased mPFC DA and greater DA turnover in the mPFC and NAc were correlated with increased anxiety-like behavior and anhedonia. Susceptible female rats exhibited increased NE concentrations in the vHIPP four weeks after PSS, in agreement with results in male rats one day after repeated predator and psychosocial stress . As descNE plays a key role in the stress response and memory consolidation and is implicated in PTSD. Years to decades after traumatic exposure, male combat veterans diagnosed with PTSD show increased CSF NE levels that positively correlate with symptom severity as identified by the Clinician-Administered PTSD scale . The perAs seen in We found that Susceptible females exhibit decreased mPFC DA concentrations, in agreement with some studies in males  but not 158Met polymorphism is associated with lowered enzyme activity and increased susceptibility to develop PTSD after stress exposure in humans [It is well-established that the DA system is activated in response to acute and chronic stressors ,53,54 ann humans . Elevated DA turnover in Susceptible females was also observed in the NAc. However, studies in males find no difference in NAc DA turnover following stress exposure 2 to 5 days after ,31. ElevThough no phenotypic effects on DA or its metabolites were observed in the dHIPP, Resilient and Susceptible rats showed decreased vHIPP DOPAC compared to Controls and there was an effect of phenotype on DA turnover. Hippocampal monoamines are dysregulated after repeated PSS exposure in high- versus low-anxiety male rats. As seen in We found a trend toward phenotypic differences in mPFC 5-HT, with Controls displaying greater concentrations. The type of stress model has clear effects on mPFC 5-HT concentrations in male rats see . Models Serotonergic projections to the PFC primarily originate in the dorsal raphe nucleus and blunted mPFC 5-HT is noted in the WKY (stress-Susceptible) rat strain . It is pIn drawing conclusions from these data, we acknowledge limitations in our design. The use of homogenized samples does not permit the determination of whether the analyte levels originate presynaptically or extracellularly. Tissue was collected at a single time point and thus did not reflect the dynamic changes in monoamine levels after a brief episode of re-exposure to the PSS context. Further, we segregated rats into Susceptible and Resilient phenotypes and eliminated rats classified as “Intermediate” . Future studies should examine dependent variables in the entire population of TMT-exposed rats to examine relationships between behavior and brain measures along a continuous scale. The lack of direct male comparisons in this study limits our ability to make definitive conclusions about the effect of sex on monoamine regulation; however, there are known baseline sex differences in monoamine turnover across brain regions. Females display lower DA turnover in the vmPFC, NAc, and dHIPP and lower 5-HT turnover in the vmPFC compared to males . FurtherAnalysis of brain monoamines after stress has previously been limited to male rodents. Using a single predator scent stress exposure rat model of PTSD, we identified phenotype-specific alterations in monoamines, their metabolites, and turnover rates in female rats related to anxiety-like behavior and anhedonia and consistent with clinical research. The most pronounced findings between phenotypes were observed in the mPFC, NAc, and vHIPP, finding susceptibility to stress was accompanied by reduced DA and HVA in the mPFC, increased DA turnover in the mPFC and NAc, and increased 5-HT turnover in the mPFC. Moreover, Susceptible rats exhibited elevated NE in the vHIPP. DA turnover in multiple brain regions was associated with susceptibility in females and was correlated with more severe anxiety-like (EPM and L-D box) behavior and anhedonia (sucrose consumption), suggesting regulation of dopamine turnover may be a contributing factor in stress-susceptibility in females.


Microbiota in colonized subjects showed higher abundance of Anaerococcus, Dialister and Peptoniphilus, while higher levels of Enterococcus, Ochrobactrum and Staphylococcus were found in non-colonized ones. Moreover, LEfSe analysis suggests an initial detection of Dialister propionicifaciens as a biomarker of MDRB colonization risk. This pilot study shows that gut microbiota profile can become a predictor biomarker for MDRB colonization in SARS-CoV-2 patients.The SARS-CoV-2 infection has increased the number of patients entering Intensive Care Unit (ICU) facilities and antibiotic treatments. Concurrently, the multi-drug resistant bacteria (MDRB) colonization index has risen. Considering that most of these bacteria are derived from gut microbiota, the study of its composition is essential. Additionally, SARS-CoV-2 infection may promote gut dysbiosis, suggesting an effect on microbiota composition. This pilot study aims to determine bacteria biomarkers to predict MDRB colonization risk in SARS-CoV-2 patients in ICUs. Seventeen adult patients with an ICU stay >48 h and who tested positive for SARS-CoV-2 infection were enrolled in this study. Patients were assigned to two groups according to routine MDRB colonization surveillance: non-colonized and colonized. Stool samples were collected when entering ICUs, and microbiota composition was determined through Next Generation Sequencing techniques. Gut microbiota from colonized patients presented significantly lower bacterial diversity compared with non-colonized patients ( The new coronavirus, denominated “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2), has been the genesis of the 2019 Coronavirus Disease (COVID-19), a pandemic menace to health worldwide . Even thClostridium difficile, Pseudomonas aeruginosa, Candida species, vancomycin-resistant enterococci (VRE), and other multidrug-resistant bacteria (MDRB) to colonize the intestine [Moreover, it is well described that admission to ICUs, including in the cases of SARS-CoV-2 patients, is associated with an intestinal dysbiosis including a remarkable reduction of phylogenetic diversity in the gut microbiota . This imntestine ,13,14,15ntestine . The manntestine ,18,19.Even though it has been reported that gut dysbiosis is a common feature in SARS-CoV-2-positive patients treated in the ICU , their sEscherichia coli or non-fermenter bacteria . In addition, MDRB colonization also presented a tendency for promoting higher percentage of fever (63%) or antibiotic treatment (75%) at the time of ICU access, and mortality once inside ICU (38%), when compared with non-colonized patients.The general clinical characteristics of non-colonized and colonized groups are shown in p < 0.01 and p < 0.05 respectively), whereas no significant modification was observed when Shannon index (richness) was considered  plots for Bray–Curtis and both unweighted and weighted UniFrac distances were employed to establish the beta diversity. Accordingly, PCoA plots indicated that the two groups were very closely clustered, with no significant separation B–D. TherBacteroidota, Bacillota, and Pseudomonadota. Of note, the abundance of these phyla did not show significant differences between non-colonized and colonized patients .The bacterial microbiota composition in critical SARS-CoV-2 patients at the time of entering the ICU showed that the most abundant phyla in all samples were patients A. Howevelococcus B, whereapatients C. Thus, Jonquetella antrophi) was identified as specific for the colonized group. Additionally, differential abundance taxa between non-colonized and colonized samples were identified by employing a volcano plot. An increase in the abundance of some bacteria, including Prevotella timonensis, Anaerococcus vaginalis, and Dialister propionicifaciens seems to be associated with MDRB colonization, whereas there was a significant decrease in the abundance of Ochrobactrum, Corynebacterium, and Prevotella bivia in comparison with non-colonized patients   A. In genDH value B.Infections caused by MDRB colonization constitute a crucial challenge in patients treated in the ICU ,22,23. TFurthermore, it is well known that SARS-CoV-2, along with many other virus infections, modifies gut microbiota composition . In thisAnaerococcus, Dialister, and Peptoniphilus. Commonly, these bacteria have been recognized as opportunistic pathogens [Enterococcus, Ochrobactrum, and Staphylococcus. Enterococci are lactic acid bacteria comprising both pathogenic and gut symbionts. In fact, many studies have indicated that these microorganisms can produce antimicrobial compounds including bacteriocins [Ochrobactrum, they are gram-negative, non-fermenting bacteria classically related to infections both in patients undergoing treatments and subjects outside of a clinical centre with different diseases [staphylococci have been widely known as pathogenic microorganisms, being S. aureus species, a classical penicillin resistant bacterium. However, it has been also published that pre-colonization with S. aureus affects the Pseudomonas aeruginosa implantation by competitive inhibition [Clostridium, Escherichia, and Corynebacterium have also been increased in non-colonized SARS-CoV-2 patients. Interestingly, these bacteria have been associated with beneficial effects in human health. For example, some Clostridium species have been used as probiotics [Lactobacillus [Corynebacterium it has been reported that they can produce specific antimicrobial molecules [Closely related to the above, the microbiota composition of colonized SARS-CoV-2 critically ill patients in the ICU facilities used was characterized by an increase of athogens , suggesteriocins ,39. Intediseases . Howeverdiseases . Similarhibition . Thus, iobiotics  and as ebacillus , while folecules . BrieflyJonquetella atrophy was exclusively present in the faecal microbiota of colonized patients. This bacterium belongs to the Synergistetes phylum, which surprisingly is not usually abundant in the normal microbiota [Dialister propionicifaciens, Anaerococcus vaginalis, and Prevotella timonensis. These bacteria could be involved in the MDRB colonization in these patients because they have been reported to have antibiotic resistance  and have been implicated in inflammatory processes as well as SARS-CoV-2 infections  [Citrobacter europaeus and Proteus bacterium, belonging to Enterobacterales order, in non-colonized patients. Specifically, it has been published that Enterobacteriaceae members such as Citrobacter produce colicins that inhibit the growth of other species [Dialister propionicifaciens could be crucial in MDRB colonization. As mentioned above, this bacterium is recognized as an opportunistic pathogen [Aiming to find a precise biomarker for predicting MDRB colonization in SARS-CoV-2 patients, we have determined the existence of the intestinal bacterial species at the time of hospitalization in the ICU of the patients with COVID recruited in our pilot study. Remarkably, the results of gut microbiota of the 8 colonized patients revealed that crobiota . Nonethecrobiota ,48. The ginalis) ,50,51. M species . This fapathogen  and, morpathogen ,55. Howepathogen ,57.Prevotella bivia, Ochrobactrum, and Corynebacterium seemed to be associated with a better prognosis, suggesting again its implication at the time of avoiding MDRB colonization  presented a strong correlation with the presence of comorbidities. As other studies have suggested, an increase in the number of comorbidities at the time of entering the ICU can increase the risk of MDRB colonization [Dialister propiniocifaciens and, as a result, raises the chances of being colonized.Taken together, it has been revealed that determining a predictive biomarker for MDRB in these patients requires a multivariable approach. Therefore, a correlation study between gut microbiota and clinical variables was performed . Our posnization A. On thepatients B. Specifnization . Thus, iThe findings of this study must be interpreted considering some limitations. Firstly, the number of samples is limited, and although the prevalence of patients colonised by MDRB in our study is high (50%) compared to approximately 5% of patients colonised by other causes, according to our bibliography and our internal data, the limited number of patients means that the results obtained need to be corroborated by a larger study. Another limitation is that it is a single-centre study. Furthermore, it must be taken into account that the patients have received antibiotic treatment, which is one of the most important factors in the production of gut dysbiosis in patients admitted to the ICU.The study was carried out in the ICU of San Cecilio University Hospital of Granada (Southern Spain) which contains up to 20 individual rooms. Patients aged ≥18 years with an ICU stay >48 h between March and May 2021 and who tested positive for SARS-CoV-2 infection were recruited. All patients were directly admitted to the ICU except for two, which were hospitalized prior to entering the ICU. During the period of study, not all patients could be recruited. In fact, only the first two patients from whom informed consent was obtained each week were recruited. Consequently, the participation rate among all COVID patients admitted to the ICU during the study period was 38.6%.The protocol of this study was approved by the Clinical Research Ethics Committee of Granada (CEIC) . All patients gave their consent before being included in the study.® CARBA SMART and CHROMID® ESBL plates . All suspected Gram-negative colonies were analysed by Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight mass spectrometry (MALDI-TOF MS)  for species identification. Antibiotic resistance phenotypes were determined using the Microscan Walkaway 96 plus system  and observing the guidelines of The European Committee on Antimicrobial Susceptibility Testing (EUCAST) (https://www.eucast.org/eucastguidancedocuments (accessed on 1 July 2022)).This is a cross-sectional pilot study. At the time of entering the ICU, rectal swabs were collected for gut microbiota analysis and for standard surveillance for aerobic Gram-negative bacteria colonization. Colonization detection was carried out by culturing rectal swabs on selective chromogenic media CHROMIDDepending on MDRB colonization results, patients were classified as non-colonized or colonized. As for SARS-CoV-2 infection, main clinical data was registered from these patients: (i) age and gender; (ii) days since symptoms appeared, days in the ICU, and symptoms upon ICU entrance; (iii) biochemical parameters; (iv) clinical severity determined through sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health disease Classification System II (APACHE II) as well as comorbidities; and (v) care therapies and outcomes.Microbiological diagnosis of SARS-CoV-2 infection was accomplished by detection of SARS-CoV-2 RNA in respiratory samples , as earlier reported . In brieFaecal DNA was isolated according to the method reported by Rodríguez-Nogales et al. . Total DBioinformatic analysis of gut microbiota samples was carried out using QIIME2 pipeline  . DemultiStatistical analysis of microbiota data was performed in R. QIIME2 objects were loaded into R to carry out the statistical analysis . The phyOn the other hand, beta diversity differences were calculated with a Permutational Multivariate Analysis of Variance (PERMANOVA) by employing the Adonis function from the Vegan package. Eulerr and MicroViz packages were employed to make the Venn diagram analysis and to represent both heatmaps and correlation plots respectively. Values from correlation plots were calculated with the Pearson coefficient. Finally, the DESeq2 package (version “4.2”) was used to identify differences in taxa expression levels while the microbial package was used to assess possible biomarkers through linear discriminant analysis (LDA) effect size (LEfSe) with an LDA score of 3.For clinical variables, data was represented as mean ± SD when it followed a normal distribution. In contrast, for non-parametric distributions, median and interquartile range were displayed. For categorical variables, percentages were used.Anaerococcus, Prevotella, and Dialister propionicifaciens. However, in order to further prove these observations, the limitations of this study should be addressed by increasing the number of patients and following the evolution of their gut microbiota along their stay at the ICU.The present pilot study provides valuable information on changes in the gut microbiota in critical patients with the SARS-CoV-2 infection developing MDRB colonization. These results, for the first time, suggest that the composition of the intestinal microbiota in patients infected with SARS-CoV-2 at the time of admission to the ICU might be a potential biomarker for MDRB colonization. Specifically, our findings highlighted the role of two genera and one species as possible biomarkers of MDRB in infected subjects:


Far-field chemical microscopy providing molecular electronic or vibrational fingerprint information opens a new window for the study of three-dimensional biological, material, and chemical systems. Chemical microscopy provides a nondestructive way of chemical identification without exterior labels. However, the diffraction limit of optics hindered it from discovering more details under the resolution limit. Recent development of super-resolution techniques gives enlightenment to open this door behind far-field chemical microscopy. Here, we review recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial resolution. We further highlight applications in biomedical research, material characterization, environmental study, cultural heritage conservation, and integrated chip inspection. This paper reviews recent advances that have pushed the boundary of far-field chemical microscopy in terms of spatial resolution as well as their applications in biomedical research, material characterization, environmental study, etc. The working principles of these techniques, however, rely on the photo-physical properties of specially designed fluorophores. Although fluorescence microscopy brings the high contrast, signal-to-noise, and specificity that is useful for the investigator, it could not be applied for imaging objects that are neither autofluorescent nor fluorescence-tagged. In addition, the introduction of fluorescent labels onto the molecular structures of interest is likely to cause both functional and structural disruption of the target molecule, possibly leading to erroneous conclusions.Breaking the optical diffraction limit has been a longstanding challenge to study the cellular- and molecular-scale activities of living systems and the nanoscale dynamics of novel materials. The 2014 Nobel Prize in Chemistry was awarded to Eric Betzig, Stefan W. Hell, and Willian E. Moerner in honor of their contribution to super-resolution fluorescence microscopy. Existing far-field super-resolution techniques have demonstrated remarkable success allowing for unprecedented spatial resolution below tens of nanometers, including stimulated emission depletion (STED)11, microsphere lens14, super oscillatory lenses16, and hyper lens20. Different from fluorescent super-resolution microscopy, label-free super-resolution microscopy is suitable for observing samples that are nonluminous and cannot be stained with fluorescent tags as in biological systems, such as carbon tubes, graphene, and integrated chips. However, these methods only provide morphological information, but not chemical selectivity. Super-resolution chemical microscopy is a useful tool that can provide valuable insights into the functionality and dynamic activities of cellular and molecular structures. By combining chemical selectivity and diffraction unlimited spatial resolution, this technique offers several advantages that can enhance our understanding of biological systems and functional materials.To circumvent the requirement of fluorescent labeling for long-duration imaging of living cells, label-free super-resolution microscopy using inherent physical properties from the samples instead of fluorescence markers as the contrast has been pursued, such as spatial frequency shift21, of which the representatives are Fourier-transform infrared absorption (FTIR) microscopy , mid-IR photothermal (MIP) microscopy, mid-IR photoacoustic microscopy, spontaneous Raman microscopy, stimulated Raman scattering (SRS) microscopy, and coherent anti-Stokes Raman scattering (CARS) microscopy. Based on electronic transitions, transient absorption (TA) microscopy has been exploited to study ultrafast electronic-state dynamics of materials, such as graphene, using the mechanism of stimulated emission (SE), ground state depletion (GSD), or excited state absorption (ESA). Improving the spatial resolution is an essential step toward the ultimate goal of chemical microscopy of single molecule or single chemical bond. It is believed that studying the changes in chemical composition at the nano to meso length scales is vital for gaining insights into the structure, function, and interaction with the environment. Therefore, the significance of nanoscale chemical microscopy cannot be overstated as it is utilized in a variety of fields such as molecular biology, medicine, material science, and chemical science.Chemical microscopy has been extensively used in qualitative and quantitative analysis of biological specimens and novel materials by studying the interaction between light and matter without the requirement of fluorescent labeling. As shown in Fig. λ = 5 μm and NA = 0.8). Near-field chemical imaging techniques are developed to solve this dilemma, by combining an atomic force microscope (AFM) with an infrared source24. In scattering-scanning near-field optical microscopy (s-SNOM), scattering from the AFM tip is collected25. In AFM-IR, absorption of IR radiation by the sample causes a thermal expansion, which deflects the AFM cantilever. The collection of high-spatial-frequency information by the near-field probe allows a spatial resolution of ~20 nm26, which breaks the diffraction limit by ~150 times. Raman scattering-based near-field chemical microscopy was also developed. Tip-enhanced Raman scattering (TERS) is realized by combining tip-scanning imaging with Raman spectroscopy30, where the metal tip not only provides a sub-10-nm high-resolution collection, but also enhances the Raman scattering signal by 4 to 7 orders. In these near-field modalities, however, the probe must be carefully controlled to be contacted with or 5–10 nanometers above the sample surface, which increases the complexity of the instrument and operation.Compared with microscopy in the visible range, chemical microscopy, especially IR absorption-based CM, usually uses longer excitation wavelengths spanning from 0.5 μm to 10 μm, which makes it challenging to acquire high spatial resolution in chemical microscopy. The spatial resolution of an optical microscope can be defined by the Abbe diffraction limit of λ/(2NA), where λ is the wavelength of the light and NA is the numerical aperture of the focusing objective. Besides, the NA of the objective lens working in the IR spectrum is limited, making the spatial resolution typically to be >3 μm . Owing to the chemical specificity and high sensitivity, SRCM can provide unique advantages in material science and biomedical applications, including nanomaterial inspection and living cell imaging. Table 31, SI-Raman32, SI-SERS34, SMLM-SERS37, STED-SRS38, Nonlinear SI-CARS39, Saturated-SRS40, HO-CARS41, and ExM-SRS42.Of these modalities, Raman scattering-based far-field SRCM is developed to break the resolution of Raman microscopy. Super-resolution methods based on different mechanisms have been applied to Raman microscopy, such as SIM, SMLM, STED, nonlinearity, and ExM. These methods lead to the development of SLI-Raman47  microscopy was developed to break the infrared diffraction limit, where the energy absorbed by the mid-IR excited molecules is nonradiatively transformed into heat, which changes the local refractive index. Then, the optical-path-length change is measured with a visible probe light.Compared with Raman scattering, IR absorption possesses a larger cross-section and is highly sensitive in the fingerprint region. A pump-probe technique termed as MIP51, Nonlinear differential TA52, and SI-TA53.For electronic-state transient absorption microscopy, the depletion effect, nonlinearity, and structured illumination are applied to realize a much higher resolution. These methods lead to the development of STED-TAIn this review, we summarize recent advances in far-field super-resolution chemical microscopy and discuss the potentials in further pushing the spatial resolution of chemical microscopy. In the following sections, we first discuss the principles and optical implementation of various far-field SRCMs. Next, we summarize the applications of far-field SRCMs in biomedical study, material study, and integrated chip inspection. Finally, we discuss potentially progressive approaches and remaining challenges in this ever-growing and fast-developing field.54. However, the spatial resolution of FTIR imaging is constrained by the diffraction limit of IR photons. In addition, strong water absorption restricts the application for observing subcellular structures in living biological samples. Although the resolution can be improved by using solid immersion lenses56, the resolution can only reach around λ/2.6, which is not enough to study the subcellular structures and activities in living systems. The recent development in photothermal IR microscopy fills this gap.Mid-infrared (MIR) spectroscopic imaging is commonly performed by using an FTIR spectrometer equipped with an infrared focal-plane array detector57. Photothermal microscopy showed superb detection sensitivity that allows single nanoparticle (1.4 nm diameter)59 and single molecule imaging60. Photothermal microscope in the visible spectrum has achieved a resolution of 90 nm by probing the nonlinear photothermal lens effect61. The mid-infrared photothermal (MIP) microscope recently developed by the Cheng group44 and the Hartland group62 breaks the infrared-wavelength diffraction limit with a pump-probe configuration, that is, using shorter-wavelength probe light to detect the temperature rise induced by IR absorption.Photothermal spectroscopy was reported in the 1970s to detect the thermal lensing effect induced by the absorption of the pump beam at focus using a probe beamT) in the sample, which leads to local refractive index change Ei can be expressed as 63, optical phase64, and interferometric scattering66. Below we discuss the various MIP configurations  chemical imaging of living cells or microorganisms via a confocal mid-IR photothermal microscope with a Cassegrain objective (0.65 NA)62. The main advantage of this approach is that the visible probe beam can go through a high-NA refractive objective instead of low-NA reflective objectives that are typically used for IR sources, which improves the lateral resolution to ~300 nm. The forward-detection mode in the counter-propagation configuration is more sensitive to transparent samples, including cells and tissues, while the epi-detection mode in the co-propagation configuration is more suitable for small samples, such as bacteria and subcellular structures.An alternative approach is to deploy a high NA objective for the counter-propagating probe beam. As shown in Fig. 47. Besides the refractive index change in photothermal effect, the Grüneisen parameter (Γ) of the material also changes with respect to the local temperature, leading to IR modulation of photoacoustic signals. As shown in Fig. The spatial resolution of MIP imaging can be further improved by UV photoacoustic detection68. This method was a further achievement in pushing the resolution of MIR photothermal microscopy and it does not require special absorbers as it relies on general absorption processes such as electronic (E) and vibrational (V) absorption.The resolution of the optical diffraction limit could be further broken by temporally probing the spatially inhomogeneous photothermal relaxation, which is recently presented by Fu et al.The photothermal process stimulated by the MIR pulse involves a heating phase due to vibrational absorption and an energy dissipation phase due to heat dissipation and mechanical waves. Since more energy was converted into acoustic waves towards the edge due to expansion, the resulting temperature increase is different in space and time even for homogenous absorbers. Therefore, a higher frequency is probed at the edge when sensed by the probe beam focus, showing a faster depletion. The difference in frequency of the center and edge could be detected with a conventional lock-in amplifier. The high resolution is closely related to the higher harmonic orders since the center-to-edge ratio shows an increasing trend at higher harmonic orders. By extracting high-harmonic components from the fast Fourier transform (FFT) of the temperature profile, the resulting images show a substantial improvement in resolution. In this work, the label-free bond-selective E- PEARL imaging of Gold nanoparticles with a resolution of 280 nm and V-PEARL imaging of living cells with a resolution of 120 nm (at the tenth harmonic) have been successfully demonstrated.43) makes it challenging to capture moving objects or for high-throughput detection. Below we discuss recent advances in improving the imaging speed and resolution by transforming point scanning into widefield schemes44.With the scanning MIP systems, there remain a few restrictions. First, most IR photons do not contribute to the signal because of the mismatch of the IR and visible focal spot size. Second, the slow speed of sample scanning 71, optical diffraction tomography (ODT)73, intensity diffraction tomography (IDT)76, yields sample-specific 2D optical-phase delay or 3D refractive-index distribution, which are the fundamental quantities used to visualize the morphology of transparent samples as in the cases of phase contrast methods. The combination of QPI and MIP provides complementary information about the sample: the chemical specificity and quantity of each molecular constituent. Recently, DH-MIP46 however, is limited by the long averaging time to achieve an adequate signal-to-noise ratio, since the phase noise, optical misalignment, and mechanical instabilities will reduce the detection sensitivity. The IDT-MIP64 method, recently presented by Cheng’s group, performs with a higher throughput of ~20 s per volume, with a lateral and axial resolution of ~350 nm and ~1.1 µm, respectively.Since the MIP process involves the change of sample refractive index and dimension, which provide optical phase contrast, widefield phase imaging with chemical bond information has been demonstrated. By simply illuminating the sample with an intensity-modulated MIR wavelength tunable laser, specific molecular distribution can be obtained by subtracting phase maps obtained by a phase contrast microscopy in the cases of MIR-on and MIR-off. These phase contrast imaging methods, however, only provide qualitative phase distribution information and have inherent disadvantages such as limited resolution, shade-off effect, and contrast inversion, which will bring additional errors. Quantitative phase imaging (QPI)IP46Fig. , has beeIP64Fig. . The spa77. As indicated in Fig. Although with great success, the sensitivity of label-free MIP imaging suffers from the weak dependence of scattering on the temperature, and the image quality is vulnerable to the speckles caused by scattering. To alleviate these shortcomings, MIP could be combined with thermosensitive fluorescent probes for the detection of photothermal effect with a much higher sensitivity64. Further pushing the resolution limit could be realized by combining MIP with label-free super-resolution methods, such as spatial frequency shift83. With the illumination of a large lateral wavevector generated by high-refractive-index chips11, the resolution of linear label-free microscopy can be improved to sub-100 nm. Applying these methods to MIP microscopy could push the spatial resolution of IR imaging to break the visible-wavelength diffraction limit.Up to now, the highest spatial resolution that has been achieved in widefield mid-IR photothermal microscopy is 0.35 µm, using 450 nm light illumination and an objective with 0.65 NAPump-probe chemical microscopy is a powerful tool for studying nonequilibrium dynamics in a variety of complex materials systems including nanostructures, low-dimensional semiconductors, and material interfaces. The simultaneous sub-micrometer spatial resolution and femtosecond temporal resolution enabled by the technique provides detailed, structurally correlated insights into intraparticle heterogeneity, free carrier transport, surface plasmon propagation, and unique interfacial states, which are inaccessible with conventional spectroscopies or microscopies.48.Label-free absorption spectroscopies are frontline techniques to reveal the spectral fingerprint, composition, and environment of materials and are applicable to a wide range of samples. In an effort to improve the spatial resolution of far-field absorption microscopy, which is limited by the diffraction of light, an imaging technique based on transient absorption saturation has recently been developedTransient absorption microscopy is implemented by a pump-probe system with contrast mechanisms including stimulated emission, ground-state bleaching, and excited-state absorption.48 is designed to decrease the probe area to below the diffraction limit in a pump-probe microscope by collinearly adding a non-modulated saturation beam, which has the same wavelength as the pump beam but with a much higher intensity. As shown in Fig. 48 using the light path in Fig. Depletion-based TA microscopy50 used a frequency-doubling crystal to generate visible spectrum light (451 nm) for saturation excitation and demonstrated a resolution down to 36 nm. The substitution of saturation photons from near IR to the visible spectrum serves two functions. Firstly, the shorter wavelength naturally forms a smaller donut-shaped ring. Secondly, the visible spectrum can more effectively clear the electronic population in the valence band of graphene.To further suppress the PSF and improve the resolution, the Wang group0  in the absence of TA saturation.It’s also possible for TA techniques to achieve subwavelength resolution without the requirement of optical transition saturation. Differential TA microscopy can measure the difference between transient absorption signals induced by two pumps, as shown in Fig. 0 Fig. , the difove Fig. , and the51 impressed an additional pump laser in a donut shape with antiphase modulation and thus form a modulated subdiffraction-limit focus center in the laser focus, as shown in Fig. To achieve super-resolution imaging of interweaved copper wires, Yang et al.84. A nearly two-fold reduction in 2D spatial resolution was achieved experimentally, from 223 nm to 114 nm by imaging the silicon nanowire53. The structured transient absorption microscopy could further be extended to 3D super-resolution by combining 3D coherent imaging theory with pump-probe microscopy85, Tzang et al.87 demodulate the reflection intensity at the corresponding harmonic frequencies  in a lock-in amplifier. The nth harmonics components of the reflectivity scale with the nth power of the excitation. Accordingly, the related effective PSFpp comprises the product of PSFprobe and that of the pump laser to the power of the nonlinearity order n, PSFpp scales down by 2)86 with a spatial resolution down to 85 nm87.In addition to transmission mode, the TA super-resolution microscopy could be applied in reflection mode by photo-exciting temperature and/or carrier changes and probing of reflectance changes. The nonlinear components of reflectance with respect to photoexcitation allow the dramatic narrowing of the effective PSF. To extract the nonlinear components of reflection due to photo-modulation  light as excitation source , the lateral spatial resolution can only reach ~300 nm90. Taking advantage of nonlinear optics, visible SRS microscopy shows improved spatial resolution with visible-wavelength pump and Stokes beams. By doubling the frequencies of the pump and Stokes beams to the wavelength around 450 nm, a spatial resolution of ~130 nm was achieved with a high-power oil immersion objective (NA = 1.49)92. Despite these advances, the spatial resolving capability is still limited by Abbe’s diffraction barrier. Below we broadly overview several super-resolution Raman imaging techniques using, structured illumination, signal blinking effect, depletion (or saturation) effect, high-order nonlinearity, and sample expansion.Raman microscope is a versatile vibrational technique that is complementary to the IR microscope. Raman microscope detects the inelastically scattered light (Stokes) from vibrations which involve a change in the molecular polarizability. As water is a weak Raman scatterer, Raman microscopy is naturally suitable for living specimens. Coherent Raman scattering (CRS) microscopies, including SRSAmong the different super-resolution techniques, the structured illumination method is an ideal first choice for spatial resolution improvement in Raman microscopy as it does not impose special optical properties, such as switching, on the choice of samples. The principle of structured illumination is to project a fine illumination pattern on a sample to expand spatial frequencies resolvable by the optics without spoiling the analytical advantage of Raman microscopy.31 shown in Fig. In the structured-line illumination-Raman (SLI-Raman)32 could also be implemented in the wide-field scheme by combining wide-field SIM with a Raman band selecting module inserted between the objective lens and the tube lens39 was derived for reconstructing super-resolution CARS images combined with nonlinear coherent. The results demonstrate the method promises a benefit on CARS microscopy by adding the super-resolution capability to improve its 2D spatial resolution by a factor of ~3.A computational scheme94 and photoswitching95. Super-resolution SRS imaging in biological samples based on a saturated SRS technique has recently been demonstrated by combing with the virtual sinusoidal modulation (VSM) method40. The nonlinear signal can also be detected by point-scanning process with a result of squeezed PSF. The higher-order nonlinear process (χ(5), χ(7)) was demonstrated to dominate over the cascaded lower-order nonlinear process under a tight focusing, which brings a much smaller excitation volume, giving rise to a significantly improved spatial resolution in the so-called HO-CARS microscopy41. The spatial resolution of ~190 nm has been realized by HO-CARS microscopy on unlabeled biological samples with 10 times decreased excitation power compared with saturated SRS technique97.Another way of further improving the resolution is to exploit non-linear properties of the material, such as the saturable absorption98, pairs of organic fluorophores as activator and reporter (STORM)5, or conventional fluorescent probes (dSTORM)100.SMLM was proposed to overcome light’s diffraction barrier using photoactivated or photo-switchable molecules to resolve the high density of molecules with super-resolution. This approach employs stochastic activation of fluorescence to switch on individual molecules and then images and bleaches them, temporally separating molecules that would otherwise be spatially indistinguishable. Merging all the single-molecule positions obtained by the photoactivation and imaging/bleaching cycles yields a final super-resolution image. The essential part of SMLM lies in the fluorophores that could be photoactivated or reversibly photo-switched by irradiation with light, such as photoactivatable or photoconvertible fluorescent proteins (PALM and FPALM)The application of the SMLM principle in label-free microscopy is quite restricted to the scarcity of blinking behavior. However, one exception is in SERS, which experimentally shows a rapid blinking behavior in the Raman spectral signal enhancement instead of signal emission Fig. . With th101:r0 is the standard deviation of the point spread function, N is the total number of photons collected, q is the size of an image pixel, and b is the background noise per pixel. For single blinking events resulting in ~105 visible photons, the position localization is expected to be on the order of a few nanometers.The localization precision of point-like objects in two dimensions can be described as35 reported the first SMLM-SERS by using metal-insulator-metal spontaneously organized metasurfaces for a substrate 36 or simple optical diffuser37, with which the hotspots can be dynamically shifted  of the substrate, which limits the application to only samples directly in contact with the plasmonic surface, such as cell membranes. Besides, the large number of raw images (a few thousand) required for reconstructing a high-resolution image also imposes limitations on the imaging speed.103, STED can achieve sub-50 nm resolution by shrinking the PSF of the imaging system using the nonlinear stimulated emission response of fluorophores. In the fluorescence-based STED, double beams of laser are used, one is a Gaussian beam for exciting the fluorophores, and the other is a doughnut-shaped ‘STED’ beam tuned in wavelength for silencing (depleting) the peripheral fluorophores in the diffraction-limited excitation regions via stimulated emission. The emission wavelength of the STED beam is red-shifted compared to that of the excitation laser and spontaneous emission fluorescence, thus only excited fluorophores located at the very center are selectively allowed to spontaneously emit fluorescence photons, the intensity distribution of which determines the PSF and spatial resolution of the system. As a result, by scanning the coaligned beams, a high-resolution image can be formed by stacking together fluorescent signals emitted from the very central region, of which the spatial resolution can be increased with higher saturation intensity.As a fluorescence-based nanoscopy38 proposed a similar way in STED to realize the depletion-based Raman based on femtosecond stimulated Raman spectroscopy (FSRS)104, which is a nonlinear four-wave mixing technique utilizing vibrational coherences to generate stimulated Raman signals. The proposed method uses a broadband probe beam (830 − 1000 nm) and a picosecond pump beam (centered at 800 nm) to generate the Gaussian-shaped SRS signal along with a femtosecond doughnut-shaped depletion beam at the pump wavelength for turning off the outer edges of the Gaussian-shaped SRS signal. The detected Raman signal has a different wavelength from the probe light and could be filtered out and sent into a spectrograph for detection. The depletion of Raman signal from this method is nonlinear with increased depletion power, resulting in significant resolution enhancement of up to 47% when scanning the edge of a Raman-active diamond. The actual suppression mechanism was stated to be an “alternative four-wave mixing” pathway. The suppression of CARS signal can also be realized by using a three-beam double SRS scheme106, in which two different SRS processes by pump-Stokes and pump-depletion beam pairs are involved to competitively consume a limited number of common pump photons.For STED imaging, the critical technique is the way to switch off the signal light, which for a long time wasn’t found in Raman imaging. In 2015, Silva et al.107. Anchored to the gel, fluorescent probes107, proteins109, RNA110, DNA111, and lipids113 can be isotropically separated in space to greater distances, and therefore can be effortlessly revolved even by conventional diffraction-limited microscopes. For example, a resolution of 25–70 nm can be achieved with a conventional confocal microscope114, according to the expansion factors of 4–10×.While most optical super-resolution microscopies focused on manipulating illumination laser or fluorophores to bypass the diffraction limitation, expansion microscopy (ExM) physically expands the specimens by embedding them in a swellable polymer network115, proExM108, iExM116, click-ExM117, U-ExM118, pan-ExM119, Cryo-ExM121). Meanwhile, the combination of expansion microscopy with other super-resolution microscopies  and SRS127 have been demonstrated. The combination of ExM with SRS, which is termed vibrational imaging of swelled tissues and analysis (VISTA), achieves a decent three-dimensional SRS imaging with a resolution down to 78 nm through optimal retention of endogenous proteins, isotropic sample expansion, and deprivation of scattering lipids42 vibrations from endogenous proteins at 2940 cm−1 for visualizing protein-rich structures amine) hexanoic acid (AcX) and strong protease digestion, to achieve ~ 100% protein retention rate during the sample expansion. With this method, up to 10× expansion and sub-50 nm resolution, SRS nanoscopy can be realized with multiple Raman and fluorescent probes tagged.Since the rise of sample expansion, ExM has been constantly iterating with many variations . The intensity ratio between the amide I band and amide II band was studied to discriminate different virus types66, as shown in Fig. Virus is a kind of microorganism consisting of a nucleic acid molecule (DNA or RNA) and a protective coat (protein) in sub-micrometer size. The detection of the virus, such as SARS-CoV-2130 in microorganism. SRCM is an attractive technique for meeting this requirement benefitting from the advantages of high resolution, high speed, high reliability, and low cost. The MIP imaging of various bacteria, such as S. aureus  are usually used as the imaging target for higher signals  and cholesterol (1669 cm−1).The cellular compositions and metabolism of living cellss43 Fig. , bladder 64 Fig. , S. cere 64 Fig. , and SKOals Fig. . Also, S−1 was typically chosen to map the protein contents64  and 2848 cm−1 (green) correspond to amide-I and CH2 stretching vibrational modes in the brain slice. MIP-based histopathology has the potential to provide real hematoxylin and eosin (H&E) results with similar visible wavelength spatial resolutions. Schnell et al. demonstrated the whole breast tissue microarray slide with MIP imaging at discrete IR wavelengths and developed the classification algorithm to differentiate cell subtypes137, as shown in Fig. SRCMs have also drawn great interest in digital histopathologyned Fig. . The 168140 by mapping protein distribution inside a primary neuron at 1650 cm−1, as shown in Fig. 140.SRCM shows the potential for studying brain pathologies, such as amyloid plaques in Alzheimer’s disease. MIP was demonstrated to characterize amyloid structures present inside neurons at sub-micrometer resolution. Evidence of localized β-sheet elevations and the polymorphic nature of β-sheet structures at the subcellular level in AD transgenic neurons were provided43 plastics (MNPs) released from polymer-based products are ubiquitous in the environment and bring environmental and human health risks. SRCM has been demonstrated to be a decent tool for studying MNPs because of the sub-micrometer resolution, high sensitivity, and chemical imaging ability. With the help of MIP, Kniazev et al.res Fig. . They al 147Fig. .149  and disordered Zn-soap phases  were found to coexist within the 0.1 μm3 volume probed  microscope, direct super-resolution imaging with a lateral resolution of 60 nm was demonstrated on imaging the multilayered copper interconnects in CPU 152, and ptychographic X-ray laminography (PyXL)153, SRCM method opens the possibility of providing easy, fast, and large-scale ICs inspections.SRCM can also be applied in the inspection of ICs in central processing unit (CPU) chipsCPU Fig. . Compare50.SRCMs are capable of revealing the chemical landscape of cells  or materials at the submicrometer level without tagging or genetically encoding fluorescent labels. Up to now, the best resolution performance was achieved to be 10 nm by SMLM-SERS, among various far-field SRCMs. In parallel, STED-TA achieved a sub-50 nm resolution by inducing a visible saturation laserHowever, a trade-off exists between resolution and other imaging performances, such as imaging depth, acquisition speed, field of view, and spectrum channels. Such a trade-off arises from the limited information throughput. For example, SMLM-SERS and SI-SERS could achieve a high resolution but sacrifice the imaging depth for using evanescent illumination. In addition, for these methods, more acquisition time is required when pursuing a better resolution. Continuing developments in spectroscopy and microscopy will render SRCM a more powerful chemical-imaging platform in the foreseeable future.155 for SRCM imaging. Another possible way of solving this problem is quantum photon correlations156. The experiment has been performed by combining coherent Raman scattering microscope with quantum correlation technique, which allows imaging of molecular bonds within a cell with a 35 percent improved SNR compared with conventional microscopy157.The detection sensitivity undermines the SNR and imaging speed especially when the resolution is down to the nanometer scale. In chemical microscopy, the sensitivity is fundamentally limited by the shot noise induced by randomness in the times that photons are detected. For samples with weak or without characteristic vibrational features, one possible way of improving the sensitivity is to apply small tags158. While the IR absorption-based SRCMs have achieved much success and found many applications from biomedical study to material science, the best resolution achieved is still limited to the probe light (around 300 nm62). We expect MIP could be integrated with SIM to break the visible diffraction limit.To further push the resolution, the key lies in the integration of chemical microscopy with newly developed super-resolution techniques or algorithms. Raman scattering-based chemical microscopies have been physically studied and combined with various super-resolution methods such as SIM, SMLM, and STED. However, the application of these SRCMs is either limited by high illumination intensity or a long acquisition time. Recently reported deconvolution algorithm-based super-resolution SRS microscopy indicated the possibility of further reducing the resolution down to sub-60 nm by using the newly developed algorithm131. For example, secondary ion mass spectrometry (SIMS)159 and X-ray fluorescence161 can be combined with SRCM to correlate elemental, isotopic, and molecular information in two or three dimensions162. As a highly efficient way to excite molecular vibration, SRCM can also be coupled with other physical processes and observables, such as optical coherence tomography164, optical diffraction tomography165, photo-induced force microscopy166, atomic force microscopy167, and electron microscopy168, which could offer benefits in terms of penetration, resolution or imaging contrast.Moreover, scatter-based and absorption-based SRCMs can be combined with each other or other chemical imaging techniques to provide complementary information on the samples169. Quantitative analysis, such as multi-variate curve resolution, previously used for the de-composition of hyperspectral stimulated Raman scattering data, can be applied to extract both the spectral profile and the concentration map of chemical content.Lastly, the large-scale application requires high-throughput multispectral imaging, which will benefit from the advanced spectral reconstruction methods, combing with computational algorithms such as deep learning methodLooking forward, we envision that advanced SRCM imaging and analysis will be a major force in future biological, medical, chemical, and material discovery.


Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting.We present a deep learning-based method—GRAph Convolutional nEtwork feature Selector (GRACES)—to select important features for HDLSS data. GRACES exploits latent relations between samples with various overfitting-reducing techniques to iteratively find a set of optimal features which gives rise to the greatest decreases in the optimization loss. We demonstrate that GRACES significantly outperforms other feature selection methods on both synthetic and real-world datasets.https://github.com/canc1993/graces.The source code is publicly available at  Many biological data representations are naturally high-dimensional and low-sample size (HDLSS) . RNA seqelopment . Howeverelopment .A useful technique in dealing with high-dimensional data is feature selection, which aims to select an optimal subset of features. Although the selection of an optimal subset of features is an NP-hard problem , variousIn this article, we propose a graph neural network-based feature selection method—GRAph Convolutional nEtwork feature Selector (GRACES)—to extract features by exploiting the latent relations between samples for HDLSS data. Inspired by DNP, GRACES is a deep learning-based method that iteratively finds a set of optimal features. GRACES utilizes various overfitting-reducing techniques, including multiple dropouts, introduction of Gaussian noises, and F-correction, to ensure the robustness of feature selection. We demonstrate that GRACES outperforms HSIC Lasso and DNP (and other baseline methods) on both synthetic and real-world datasets.The article is organized into six sections. We perform a thorough literature review on feature selection  in Section 2. The main architecture of GRACES is presented in Section 3. We evaluate the performance of GRACES along with several representative methods on both synthetic and real-world datasets in Section 4. We perform an ablation study and discuss the drawbacks of GRACES in Section 5. Finally, we conclude with future research directions in Section 6.Univariate statistical tests have been widely applied for feature selection . The coml1-norm of feature weights. Lasso produces a sparse solution with which the weights of irrelevant features are zero. Yet, Lasso fails to capture nonlinear dependency. Therefore, kernel-based Lasso such as HSIC Lasso (l1-regularizer in Lasso can be compatibly incorporated into different classifiers such as logistic regression (LR Lasso) for feature selection , has a powerful built-in feature selection capability for HDLSS data . Lasso aIC Lasso , 2018 haIC Lasso  to find IC Lasso , minimumIC Lasso , sparse IC Lasso , quadratIC Lasso , and cenIC Lasso . Additioelection .Decision tree-based methods are also popular for feature selection, which can model nonlinear input–output relations . As an eNumerous deep learning-based methods have been proposed for feature selection . Like deOther frequently used feature selection methods include recursive feature elimination  and sequX and index it by zero. The total number of features now is p + 1, and the original features have the same index numbers as before. We initialize the selected feature set GRACES is an iterative algorithm which has five major components: feature initialization, graph construction, neural network, multiple dropouts, and gradient computation . Motivati and j, the cosine similarity is defined as the cosine of the angle between them in the Euclidean space, i.e.For graph construction, we exploit the cosine similarity measure based on the selected features in δ (which is a hyperparameter of GRACES). The resulting similarity graph captures the latent interactions between samples and will be used in the graph convolutional network (GCN) layer. The similarity graph is different at each iteration, and other similarity measures, such as Pearson correlation and Chi-squared distance  such that the h2 denotes the second hidden dimension), and j. GraphSAGE leverages node feature information to efficiently generate embeddings by sampling and aggregating features from a node’s local neighborhood  without a bias vector, i.e.where raphSAGE  to refinhborhood . Finallym times with dropout probability P (m and P are hyperparameters of GRACES). In other words, we obtain multiple different dropout neural network models. The technique of multiple dropouts has proved to be effectively stable and robust for deep learning-based feature selection under the HDLSS setting end while12: 13: Drop the bias feature (i.e. the first element) from Return: Selected feature set 14: To further reduce the effect of overfitting due to low-sample size, we incorporate two additional strategies in GRACES. First, we consider introducing Gaussian noises to the weight matrices of the GCN layer, i.e. adding noise matrices generated from a Gaussian distribution with mean zero and variance j is given byjg is the normalized score computed from jf is the normalized score computed from the F-statistic, and For gradient computation, we compute the gradient regarding the input weight for each dropout neural network model and take the mean, i.e.training . Second,t-test, the Pearson correlation test, and the Wilcoxon signed-rank test, can be applied similarly. More advanced methods like HSIC Lasso or DNP can also be considered, but might require more computational recourses. The two overfitting-reducing strategies effectively improve the performance of GRACES for HDLSS data, see Section 5.The reasons we select the ANOVA F-test are: (i) it is computationally efficient; (ii) it achieves a relatively good performance in feature selection for some HDLSS data; (iii) it does not suffer from overfitting, so including it can reduce the effect of overfitting in GRACES. Other statistical tests, such as the Student’s Detailed steps of GRACES can be found in https://github.com/riken-aip/pyHSICLasso) (https://github.com/unnir/CancelOut)  (https://github.com/KaixuYang/ENNS)  , RF (BreICLasso) , CancelO method) , and DNPng/ENNS) . HSIC Lascikit-learn function make_classification to generate synthetic data. The function creates clusters of points normally distributed about vertices of a q-dimensional hypercube (q is the number of important features) and assigns an equal number of clusters to each class (class_sep). We randomly split each dataset into 70% training, 20% validation, and 10% testing with 20 replicates. We performed grid search for finding the optimal key hyperparameters for each method. We reported the average test AUROC (over 20 times train test splits) with respect to the total number of features. In the meantime, since we know the exact important features, we also reported the correction rate of the selected features during training.We used the ch class . We set The results are shown in We used the same biological datasets from the DNP paper , which iColon: Gene expression data from colon tumor patients and normal control;Leukemia: Gene expression data from acute lymphoblastic leukemia (ALL) patients and normal control;ALLAML: Gene expression data from acute lymphoblastic leukemia (ALL) patients and acute myeloid leukemia (AML) patients;GLI_85: Gene expression data from glioma tumor patients and normal control;Prostate_GE: Gene expression data from prostate cancer patients and normal control;SMK_CAN_187: Gene expression data from smokers with lung cancer and smokers without lung cancer.The statistics of the datasets are shown in P-value < .05, one-sample paired t-test on the total 400 AUROC scores). Moreover, the performance of GRACES is stable and robust across all the datasets, while the other methods  would fail on certain datasets , see t-test on the total 2400 AUROC scores). Surprisingly, the ANOVA F-test achieves a relative good and stable performance on the real-world datasets. RF and CancelOut, which are not suitable for HDLSS data, do not perform well. We further repeated the experiment with MLP and k-nearest neighbors as the final classifiers and observed a similar result, where GRACES achieves a comparable or improved performance over the baselines on the six biological datasets . Hence, GRACES might fail on data with highly nonlinear relations between features and labels. Second, GRACES is computationally inefficient. We computed the total computational time of each method for running the six biological datasets with selected features from 1 to 10, see Next, we discuss two drawbacks of GRACES. First, according to the experiment on the synthetic datasets, although GRACES outperforms the other baseline methods, its performance also declines when the two classes are mixed intricately (i.e. the variable In this article, we proposed a deep learning-based method GRACES to perform feature selection on HDLSS data. By utilizing GCN along with different overfitting-reducing strategies including multiple dropouts, introduction of Gaussian noises, and F-correction, GRACES achieves a superior performance on both the synthetic and real-world HDLSS datasets compared to other classical feature selection methods. GRACES can be applied to many other types of biological datasets that suffer from the HDLSS problem. It will be useful to investigate more sophisticated network architecture to learn the low-dimensional representations of data. For example, hypergraph convolutional network , generalbtad135_Supplementary_DataClick here for additional data file.


The cholinergic system is altered in schizophrenia. Particularly, patients’ volumes of basal-forebrain cholinergic nuclei (BFCN) are lower and correlated with attentional deficits. It is unclear, however, if and how BFCN changes and their link to cognitive symptoms extend across the schizophrenia spectrum, including individuals with at-risk mental state for psychosis (ARMS) or during first psychotic episode (FEP).n = 4) comprising 68 ARMS subjects, 98 FEP patients (27 unmedicated and 71 medicated), 140 patients with established schizophrenia , and 169 healthy controls.To address this question, we assessed voxel-based morphometry (VBM) of structural magnetic resonance imaging data of anterior and posterior BFCN subclusters as well as symptom ratings, including cognitive, positive, and negative symptoms, in a large multi-site dataset  and non-linear (larger vs. lower VBM) differences in BFCN across the schizophrenia spectrum, which are specifically associated both with medication, including its anticholinergic burden, and cognitive symptoms. Data suggest an altered trajectory of BFCN integrity in schizophrenia, influenced by medication and relevant for cognitive symptoms. Both receptor types are distributed throughout the brain, and their subtypes are localized pre- and postsynaptically.15 ACh release throughout the brain is mediated by both widely distributed cholinergic interneurons and cholinergic projecting neurons, which are mostly located in the brainstem cholinergic nuclei and the basal-forebrain cholinergic nuclei (BFCN).16 The BFCN neurons project to cortical and subcortical structures following a distinct topographical organization and contribute to basic cognitive functions, including attention and working memory.17 Cytoarchitectonically, BFCN neurons are divided into 4 main cell groups; furthermore, by the use of functional connectivity analyses, the human BFCN can be divided into an anterior part—including medial septal nucleus, diagonal band of Broca, and anterior-medial parts of the nucleus basalis of Meynert that project to the hippocampus, ventromedial prefrontal, and retrosplenial/posterior cingulate cortex,—and a posterior part—comprising the remaining parts of the nucleus basalis of Meynert, which project to the rest of the brain including amygdala nuclei.19Schizophrenia is heterogeneous not only regarding its symptoms and their course but also at the level of underpinning genetics and pathophysiological pathways, including abnormalities in neuromodulatory systems.12 Altered cholinergic transmission has been reported both by postmortem findings of reduced numbers of ACh receptors,21 and in vivo imaging studies, using single-photon emission computed tomography or positron emission tomography, demonstrating decreased availability of ACh receptors in schizophrenia.22–25 Furthermore, pharmacological manipulation of the cholinergic system in patients demonstrated improved memory and attention and enhanced activation of multiple brain regions involved in cognitive functioning.26 Treatment with a muscarinic cholinergic receptor agonist improved not only patients’ positive symptoms but also negative symptoms and their global clinical impression.27 Recently, we demonstrated that the structural integrity of the whole BFCN cluster is altered in SCZ.28 Specifically, we found that magnetic resonance imaging (MRI)-based voxel-based morphometry (VBM) measures of BFCN were decreased in two independent samples of patients.28 It remains, however, elusive both when the BFCN alterations appear in the course of schizophrenia and to what extent they depend on the stage of the disorder, including chronicity and long-term treatment. For instance, there is evidence that early cholinergic alterations are relevant for psychosis and its onset.29 To address this question of occurring BFCN changes along the schizophrenia course indirectly , the current study focuses on the BFCN subdivisions’ structure and its alterations across the stages of the schizophrenia spectrum. Due to the relevance of cholinergic changes for psychotic states, we hypothesized that VBM of both anterior and posterior BFCN are not only altered in SCZ but also in earlier stages.Mounting evidence suggests that several aspects of the cholinergic system are altered in schizophrenia.28 This result is consistent both with previous studies demonstrating a link between aberrant cholinergic transmission and cognitive deficits in schizophrenia as well as animal and human studies demonstrating the general relevance of the cholinergic system for cognitive functioning.12 In particular, animal studies have shown that the structural integrity of the BFCN and their projections are crucial for several cognitive functions.30 Human evidence, based on in vivo neuroimaging, demonstrated that BFCN structural integrity links with cognition in healthy subjects.31 While structural changes in the cholinergic system are relevant for cognitive deficits in schizophrenia, it is unclear whether a similar link exists in other stages of the schizophrenia spectrum. We hypothesized that structural alterations occur across stages of the spectrum, with potential changes in both anterior and posterior BFCN being associated with patients’ cognitive deficits.Next, regarding the relevance of these structural changes for schizophrenia symptoms, we previously found that in SCZ, lower BFCN-VBM mediates patients’ attentional deficits, measured by the Symbol-coding Task.32–37 94 SCZ and 97 HC of this dataset overlap with our previous study on BFCN volume in SCZ.28 Furthermore, we used the same VBM approach as previously,28 namely VBM. These measures of BFCN were derived from T1-weighted images by combining volumetry with a stereotactically deﬁned BFCN atlas.38–40To address these hypotheses, we studied T1-weighted MRI and performance on cognitive tests from cross-sectional multi-site data of the schizophrenia spectrum, including ARMS, FEP, SCZ, and healthy controls (HC).We studied a multi-site dataset comprising 68 ARMS, 98 FEP , 140 SCZ (medicated), and 169 HC  database (http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html) were included.Seventy-two SCZ meeting DSM-IV criteria,42 Diagnosis of SCZ and FEP was determined with the Mini-International Neuropsychiatric Interview for DSM-IV.Data of 48 SCZ, 26 FEP, and 28 age- and sex-matched HC were obtained from previous publications.35 Diagnosis of FEP was determined by the Brief Psychiatric Rating Scale as described by Yung and colleagues.44 ARMS was confirmed based on Personal Assessment and Crisis Evaluation criteria.44The sample of 78 FEP, 73 ARMS, and 44 HC was derived from previous studies.All studies were approved by the local ethics committees, and participants provided written informed consent.45 Phonemic Fluency provides information on processing speed and semantic memory based on retrieval of words beginning with a specific letter.46To assess cognitive abilities, the Trail Making Test Part A (TMT-A) or Phonemic Fluency test was used. We used two different test batteries as the four sites employed different cognitive testing, and therefore, not all tests were available for all patient groups. TMT-A mainly measures processing speed and visual attention and is reported as seconds required to solve the task.For demographic and clinical scores, see MRI acquisition parameters are described in http://dbm.neuro.uni-jena.de/cat/) and SPM12,47 implemented in MATLAB R2019b . Default parameters for preprocessing were used following the standard protocol (https://neuro-jena.github.io/cat/) unless otherwise stated. Specifically, images were segmented into gray matter (GM), white matter, and cerebral spinal fluid partitions of 1.5-mm isotropic voxel size. Each participant’s partitions in native space were registered to a stereotactic standard space . To preserve the total amount of GM within each voxel, the partitioned images were modulated by the relative voxel values  before and after normalization. The resulting GM-VBM was then smoothed with a Gaussian kernel full-width-half-maximum of 4mm due to the small size of the region-of-interests and in line with our previous approach . To control segmentation and registration accuracy, GM maps were visually inspected. Briefly, in this study, 5 ARMS (from the Basel cohort), 6 FEP (from the Basel cohort), and 6 SCZ patients  were excluded based on quality control and missing demographic information. Quality control in this study was performed based on interquartile range (IQR) output from SPM12. Images with an IQR of 5 standard derivations below and above the mean IQR were excluded. Modulated GM voxel values within a ROI encompassing the BFCN were summed to extract individual GM-VBM of the anterior and posterior BFCN, respectively, from the warped GM segments. The BFCN-ROI is based on a consensus of currently available stereotactic BFCN masks. The masks were derived from cytoarchitectonic mappings based on histology and postmortem MRI.38–40 The anterior subcluster includes the medial septal nucleus, diagonal band, and anterior-medial parts of the nucleus basalis Meynert, and the posterior subcluster contains the remaining part of the nucleus basalis Meynert  was used to normalize the individual BFCN- and GM-VBM for differences in head size, as previously described.48T1-weighted structural images from the 4 datasets were processed separately using CAT12 ( Meynert .18 This https://github.com/Jfortin1/neuroCombat). NeuroCombat adjusts data for scanner effects by using a linear regression model combined with empirical Bayes.49To enable comparison between the four acquisition sites, VBM were harmonized using the open-source toolbox neuroCombat 0.2.12 implemented in Python 3.7 (n = 475). Statistics were carried out using Python 3.10.6 and SPSS 23.0 .Analyses were performed on TIV-normalized VBM of all four combined datasets  and subgroups  in anterior and posterior BFCN and global GM-VBM were evaluated using univariate analysis of covariance (ANCOVA), controlling for the influence of age and sex. For subsequent post hoc analyses, Dunnett’s test with HC as control group was used.Control and specificity analyses were performed for effects of antipsychotic medication , anticholinergic burden (ACB) of medication, total brain volume, age, and illness duration . Anterior BFCN-VBM was lower in SCZ compared to HC , suggesting that a higher level of anticholinergic burden of medication is relevant for lower anterior BFCN measures in SCZ. Antipsychotic medication in terms of chlorpromazine equivalents (CPZ) did not correlate with anterior BFCN-VBM. Next, global GM-VBM changes affected our ANCOVA result, which remained only at-trend significant when controlling for global GM-VBM. The association between lower anterior BFCN-VBM and ACB is also not independent of global GM-VBM, indicated by partial correlation analysis. Although age correlated with anterior BFCN-VBM in both HC and SCZ, we did not find an interaction between age and group, indicating that the disorder’s effect on BFCN was not further increased by age. Illness duration did not influence the group differences in anterior BFCN-VBM.Several control and specificity analyses were conducted . Posterior BFCN-VBM was larger in FEP compared to HC , indicating that larger posterior BFCN-VBM were mainly driven by FEP-unmedicated. Group differences in posterior BFCN-VBM only remained at-trend significant after controlling for global GM. However, it should be noted that we did not find differences in global GM-VBM between HC and FEP. Nonetheless, we cannot exclude that the overall GM-VBM influences the differences in posterior BFCN-VBM in FEP.Several control and specificity analyses were conducted . A significant negative correlation was found between lower anterior BFCN-VBM in SCZ and TMT-A , indicating that lower anterior BFCN-VBM is associated with more severe cognitive deficits. We found the same correlation between anterior BFCN-VBM and other cognitive tests , suggesting the independence of this relationship from the cognitive test used. To investigate whether additional variables such as ACB, sex, and age influenced the relationship between lower anterior BFCN-VBM and TMT-A scores, we computed a multiple regression analysis (see P < .001).To investigate whether lower anterior BFCN-VBM in SCZ was linked to processing speed deficits, measured by TMT-A, both ANOVA and subsequent correlation analysis were performed . First, ysis see . ResultsSpecificity analyses were conducted to control the influence of other factors , indicating cognitive deficits. Furthermore, we identified a negative at-trend-to-significance correlation between larger posterior BFCN-VBM and Phonemic Fluency  and non-linear (larger vs. lower VBM) differences in BFCN across the schizophrenia spectrum, which are influenced by both medications, including its anticholinergic-burden, and consistently and specifically associated with cognitive symptoms. Data suggest an altered, medication-influenced, cognitive symptom-relevant trajectory of BFCN integrity in schizophrenia.28 and postmortem and single-photon emission computed tomography/PET studies demonstrating altered synaptic cholinergic transmission in SCZ.50We found significantly lower anterior BFCN-VBM in SCZ compared to HC and FEP. This is in line both with previous structural imaging findings reporting lower VBM across the whole and anterior BFCN in SCZ51 Lipid in myelin and membranes is also suggested to be crucial for the MRI contrast by shortening the relaxation time.52 In SCZ, lipid composition appears disturbed, and this disturbance might be further influenced by antipsychotic medication.53–56 Conversely, postmortem findings revealed overall reduced GM volume, especially in the limbic system and thalamus, and decreased cell density in schizophrenia.58 Therefore, the observed differences in anterior BFCN MRI-signal might result from a combination of decreased lipid levels due to both long-term treatment with antipsychotics and actual reduction in GM tissue.We interpret these differences in BFCN-VBM as reflecting volume reductions in anterior BFCN. MRI-based volumetric measurements are not a direct measure of brain structure. Changes in cells, cell number, water content, tissue perfusion, and both artifacts and confounders, such as head motion and smoking, contribute to the T1-weighted MRI signal.59–61 Group differences in anterior BFCN-VBM changed from highly significant to at-trend toward significant after controlling for global GM-VBM. Therefore, we cannot exclude that anterior BFCN-VBM decrease is parallel with general GM-VBM decrease in SCZ. Interestingly, ACB was associated with GM-VBM, indicating that while the lower anterior BFCN-VBM is not independent of global GM-VBM, both measures are influenced by the anticholinergic burden of medication in SCZ. Anterior BFCN-VBM further correlated with age in both HC and SCZ, indicating that age influences anterior BFCN-VBM. Moderation analysis showed that age did not interact with the disorder's effect on anterior BFCN-VBM, suggesting that the disorder's influence on anterior BFCN is not modified by age. Furthermore, we found neither a main effect of illness duration nor an interaction effect of age and illness duration on anterior BFCN-VBM. These findings suggest that BFCN-VBM is not altered in SCZ as a function of illness duration. This is in contrast to global GM-VBM reductions, which become more pronounced with advancing illness duration.63The group difference in anterior BFCN-VBM also seems to be influenced by anticholinergic-burden of medication. This suggests that the level of anticholinergic-burden of medication might be relevant for changes in the cholinergic system in SCZ, indicating a link between lower BFCN-VBM and cholinergic transmission. We found lower global GM-VBM in SCZ—a result consistently reported by previous neuroimaging studies.64 suggesting that changes in anterior BFCN are already present in FEP but may only be detected at the volumetric level at a later stage.We found no anterior BFCN-VBM differences in ARMS and FEP compared to HC. While a lack of differences in early stages of the schizophrenia spectrum may indicate that changes only occur later during the illness course, we cannot completely exclude changes in earlier stages. Indeed, the ARMS group examined in this study was quite heterogeneous, and only a part transitioned to psychosis at a later time point. However, a recent multimodal-imaging study demonstrated microstructural alterations of the anterior BFCN in FEP,64 However, the two volumetric and microstructural measures differ, making direct comparisons difficult. Whether antipsychotic medication accounts for changes in VBM not only during FEP but also along the course of schizophrenia continues to be debated. For example, research has provided evidence for associations between GM reductions and higher doses of antipsychotics.66 On the other hand, some studies were not able to identify an influence of antipsychotic treatment on structural brain alterations in patients.67 Future studies need to investigate the effect of antipsychotics longitudinally to test whether antipsychotic treatment leads to alterations in the cholinergic system. Nevertheless, our findings indicate that the acute state of the first psychotic episode might induce a temporary increase in posterior BFCN-VBM independent of medication.We found that posterior BFCN-VBM was larger in FEP compared to HC. Particularly, posterior BFCN-VBM was larger in FEP-unmedicated compared to HC. Intriguingly, posterior BFCN-VBM did not differ between controls and FEP-medicated, suggesting that the group differences in posterior BFCN-VBM across the spectrum may be driven by unmedicated patients. Conversely, Park and colleagues found no differences in myelin content and axonal integrity in the posterior BFCN in FEP-unmedicated.51 As mentioned above, brain water content determines tissue contrast in MRI,68 and a recent study has shown increased whole-brain extracellular water volume in FEP.69 Thus, the stronger signal may reflect increased water content rather than changes in cell tissue. However, Lawrie suggested that psychosis leads to increased brain volume, and antipsychotic treatment might normalize these changes.70 Accordingly, the increased signal in FEP-unmedicated might reflect larger volumes, while in FEP-medicated, changes in lipid content that are affected by antipsychotic treatment result in a decreased MRI signal. To sum up, the findings of larger VBM could reflect alterations in brain structure along with changes in water content, lipid levels, and medication effects.The observed MRI-based changes might be caused by several nonstructural factors.72 but not by others.73–75 Based on our data, we suggest that larger posterior BFCN-VBM in FEP reflect a relative specific alteration that is largely independent of the average global GM-VBM.Differences in posterior BFCN-VBM were only marginally influenced by global GM. Crucially, global GM-VBM in FEP did not differ significantly from that of HC. Global GM reductions have been reported in both ARMS and FEP by some studies28 However, posterior BFCN-VBM in this SCZ sample was not altered. Assuming chronic-progressive changes in the BFCN, this contradiction might result from a lower age and illness duration in the present dataset compared to the previous one. On the other hand, antipsychotic medication might also impact alterations in the posterior BFCN. No differences in posterior BFCN-VBM were found in the ARMS group. This might again indicate that alterations only occur with the first psychotic episode.Our prior research demonstrated lower posterior BFCN-VBM in SCZ.27Regarding the clinical relevance of the detected VBM differences, we demonstrated that BFCN changes were specifically correlated with cognitive deficits. We found corresponding correlations with processing speed in SCZ for the anterior BFCN but only at-trend in FEP for the posterior BFCN. This result was independent of both the cognitive test used and age. This relation seems disorder-specific, as we did not find a corresponding relation in HC, suggesting that the conditions of the disorder are necessary for such a relationship. Furthermore, the association between BFCN and cognitive deficits was symptom-specific, as we did not find a corresponding association for negative or positive symptoms, indicating the special role of BFCN for cognitive deficits in the schizophrenia spectrum. This is in contrast to studies focused on synaptic transmission: evidence demonstrated that pharmacological intervention of the cholinergic system affects not only cognitive but also the psychotic and negative symptoms.77 While it is clear that antipsychotic medication worsens cognitive functioning via anticholinergic effects, our results indicate that, with chronic usage, these anticholinergic effects could also lead to neurobiological changes in the BFCN, which are likewise associated with cognitive symptoms. Future studies are needed to evaluate whether the impact of anticholinergic effects on cognition is brought about by structural changes observed in the BFCN.Remarkably, we observed a negative effect of the anticholinergic-burden of medication on the link between BFCN and cognitive performance in SCZ. The anticholinergic-burden influenced the correlation between larger posterior BFCN-VBM and cognitive impairments in FEP. Additionally, it negatively affected lower anterior BFCN-VBM in long-term treated SCZ. Accordingly, recent studies suggest a correlation between the anticholinergic-burden of medication and patients’ cognitive deficits.18 The prior maps were derived from combined postmortem MRI and histology.38–40 Previous studies demonstrated that these maps of the BFCN are reliable tools for accurate segmentation of the BFCN.78 Furthermore, we did not measure individual disorder progression trajectories in this cross-sectional study design. Comparisons across the whole dataset regarding clinical and cognitive measurements were not possible due to the distinct test batteries used across the four sites. Furthermore, our result regarding associations between BFCN-VBM and cognitive scores warrants caution as we did not correct for multiple comparisons. For a detailed description, see Supplemental Discussion.In this study, BFCN structure was not measured directly, as volumetric MRI does not solely reflect brain volume. The integrity of the cholinergic system was derived from stereotactic mapping, which is only an indirect marker of cholinergic neurons in the basal forebrain. We used a consensus map based on all available maps of the BFCN, which contains subdivisions based on functional parcellation.We demonstrated a complex and non-linear pattern of posterior and anterior BFCN alterations across the schizophrenia spectrum. BFCN alterations were influenced by medication, including its anticholinergic burden, and specifically associated with patients’ cognitive deficits.sbad118_suppl_Supplementary_MaterialClick here for additional data file.