Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol ( From a basic research perspective, the subjective experience of being aware of oneself and the environment most likely emerges as an epiphenomenon of cerebral information processing , 2017 anBased on this idea, our data analysis aimed to integrate different levels of hierarchy and considered information processing in the human brain to be divided into  local inLocal information processing in unconscious humans is known to be significantly diminished in the medial prefrontal cortex, precuneus, posterior cingulate cortex, superior parietal lobe, and in the dorsolateral prefrontal and inferior parietal cortices during general anesthesia when analyzed by functional MRI (fMRI) . In awakGlobal communication has been mainly investigated between specific brain areas using fMRI and EEG. Like local information processing, differences between consciousness and unconsciousness were observed mainly in the frontoparietal network and the default mode network. Specifically, information exchange between the areas encompassing these networks was significantly reduced in various studies on unconsciousness . Later, We reasoned that specific patterns of global brain communication and their dynamic change are an absolute requirement for the emergence of consciousness. In order to identify specific patterns of global brain communication, we employed a k-means algorithm on covariance matrices. The change of covariance matrices over time was regarded as the dynamics of global brain communication .The study was conducted in accordance with the Declaration of Helsinki, and the protocol of the study was approved by the ethics committee of the medical school of the Technische Universität München . The volunteers or the legal surrogates of the enrolled patients were given detailed information about the procedures and potential risks prior to written informed consent. Medical history was taken from every applicant, especially inquiring contraindications for an examination in a magnetic resonance scanner. Data were acquired between April 2013 and December 2019.n = 3), stroke (n = 13), anoxia (n = 1), and toxic/metabolic (n = 1). Under stroke, we summarized ischemic stroke as well as subarachnoid hemorrhage and intracerebral hemorrhage with no traumatic cause. We included all types of brain injuries for two main reasons: (1) we wanted to generalize the approach, and (2) we aimed to define the AWAKE state by specific brain activity and the UWS state by the absence of this activity. Based on this, we regarded the exact brain pathology as secondary. Any preexisting medication was left unchanged for the measurement accepting oral sedatives . Intravenous sedatives were not applied. The subjects fasted for 6 h prior to the measurement. The patients with UWS were rated using the Coma Recovery Scale Revisited (CRS-R) at least three times  by an experienced medical doctor. All the patients had UWS on the day of measurement. All the other volunteers  were healthy, male volunteers (aged 20–36 years) and had no contraindication for general anesthesia using propofol or sevoflurane (American Society of Anesthesiology I). All the subjects with contraindications for MRI or positron emission tomography (PET) were excluded. We included unmatched healthy controls for the patients with UWS recorded on the same PET scanner that was also used for the UWS experiments. Because of the complex nature of the experiment , we were unable to record a group of age-matched patients with cerebral pathology during the AWAKE state. This approach might introduce a bias since group differences might be affected by structural brain alterations or age differences.Eighteen patients  were acquired through intensive care units. Inclusion criteria were patients with unresponsive wakefulness syndrome (UWS) through any brain damage, i.e., traumatic  was carried out using a target-controlled infusion pump . Propofol concentration was increased by 0.4 μg/ml steps beginning at 1.2 μg/ml until volunteers stopped responding to the verbal command “squeeze my hand” . The concentration was then kept stable for the remaining fMRI measurement. This point was reached at plasma concentrations of 2.97 +/− 0.47 μg/ml (mean +/− SD). Ten minutes of equilibration time were waited before the actual measurement took place. Throughout this article, this state is referred to as PROP. Details of the propofol anesthesia protocol can be found in The resting-state was acquired identical to the propofol setting. Image acquisition during the sevoflurane-induced loss of responsiveness (in this article: SEVO) was carried out after intubation with a magnetic resonance tomograph-compatible laryngeal mask and during artificial ventilation using an anesthesia machine . Sevoflurane was kept stable at 2 volume percent end-tidal concentration during this condition. The subjects were unresponsive to the command “squeeze my hand” during this condition, tolerated the laryngeal mask well, and showed reduced movements when compared with the wakeful state. It is also noteworthy that clinically, this sedation was deeper when compared with PROP . All the participants were asked for any memories of the unresponsive state, and all reported amnesia for the procedure. Details of the sevoflurane anesthesia protocol can be found in n = 18) and healthy controls (n = 20) were acquired using an integrated Siemens Biograph mMR scanner  capable of registering concurrent positron emission tomography (PET) and MRI from 3 T data using the vendor-supplied 12-channel phase-array head coil. PET volume, two T2∗-weighted echo-planar imaging (EPI) MRI, and magnetization-prepared rapid acquisition gradient echo (MPRAGE) T1-weighted anatomic volume data were recorded. Scanning parameters of the EPI included repetition time (TR)/echo time (TE)/flip angle, 2,000 ms/30 ms/90°; 35 slices (gap 0.6 mm) aligned to the anterior-posterior commissure covering the whole brain; field of view (FoV), 192 mm; matrix size, 64 × 64; and voxel size, 3 mm × 3 mm × 3.0 mm. Each of the two measurements consisted of 300 acquisitions in interleaved mode, with a total scan time of 10 min and 8 s. Scanning parameters for the MPRAGE sequence included TR/TE/flip angle, 2,300 ms/2.98 ms/9°; 160 slices  covering the whole brain; FoV, 256 mm; matrix size, 256 × 256; and voxel size, 1 mm × 1 mm × 1 mm. The total scan time was 5 min and 3 s. The PET data were not further included in this study.Data from the patients with UWS  employing an eight-channel, phased-array head coil. fMRI was performed with a gradient EPI sequence . Anatomy was acquired before the functional scan using a single T1-weighted sequence and 1 mm × 1 mm × 1 mm voxel size per subject. Only data sets that not exceeded 2 mm of translation in either z-, y-, or z-direction were included in the subsequent data analysis.DPARSF 4.0  and SPM1In the first step, the preprocessed fMRI data were analyzed using a group-level spatial iICA  as implemented in the Gift toolbox  and GICA3 back reconstruction. The INFOMAX algorithm was employed together with an ICASSO and 20 repetitions to decompose the data into 75 spatially independent components. We used the GICA3 back reconstruction to generate individual spatial maps and associated  time courses for each individual map . We chosi; and (2) one time course for each component and each subject. The time courses were also standardized to be standard normally distributed to account for different scanners and amplitudes of the raw blood oxygenation level-dependent signal. We refer to these time courses as Ri.This resulted in two types of data: (1) spatially independent components for each subject that were z-standardized. We refer to these maps as SA signal from neuronal origin was assumed if the component projected on gray matter showed no similarity to venous vessels and if it showed a characteristic frequency spectrum with a clear peak below 0.1 Hz. Noise components usually showed a flat frequency spectrum similar to Gaussian noise. Fifty-seven functional networks were identified and included in the subsequent analysis. These networks were close to identical to the ones previously published .i time courses were despiked and low-pass filtered < 0.15 Hz -distance with 20 repetitions to assign the between-network connectivity matrices to between network-connectivity patterns. These patterns are not predefined and are generated by the k-means algorithm as clusters or patterns of the between-network connectivity matrices. The generally arbitrary number of patterns was defined to be 7 to make the results comparable to After variable ranking using the Fisher score, one to seven variables were chosen for a support vector machine. The aim was to test whether the information from the anesthesia experiments can be generalized. For training of the support vector machine algorithm, the 50 data sets from the anesthesia experiments were employed. Eighteen data sets from the patients with UWS and 20 controls were used for the test data set. We also conducted the support vector machine algorithm with a random permutation of groups of the test data set to illustrate chance level. These steps were carried out with FSLib (Version 5.1). All analysis steps were carried out in Matlab .P-values were corrected for multiple comparisons using Bonferroni’s method (multiplication factor 6). Differences in pattern distribution between the groups  were tested by using a chi-square test with Pearson’s chi-square distance. Significance was assumed if p < 0.05.All the group comparisons were conducted with the two-sided Mann–Whitney/rank sum test. n = 57) after smoothing using the mean of 30 frames sliding window. This was the same slide window length from which the covariance matrices were calculated. The time series were transformed to be standard normally distributed in each recording to remove variance or amplitude effects. Significance was assumed if p < 0.05.Logistic regression was carried out in Matlab using the “mnrfit” and logit transfer functions. The binary categorical responses of the appearance of patterns specific for consciousness (patterns 1 and 3) and all the other patterns were modeled on the time series of each individual network  and of 14 healthy volunteers during wakefulness and sevoflurane-induced general anesthesia (SEVO). The test data set comprised 20 healthy volunteers and 18 patients with UWS (see n = 57) from noise sources (n = 18). For each network, we then calculated the associated covariance matrices in 30-frame time window each , SEVO (n = 14), and AWAKE (n = 25) and one test data set containing the UWS (n = 18) and AWAKE subjects from the same MRI scanner . We classified the test data set employing a support vector machine and prior Fisher scoring for feature selection  but not for PROP vs. controls. We also observed a relevant overlap between the groups and the span of values in the AWAKE group was , thus covering the whole range of encountered values in all the groups. Consequently, the dynamics of global brain communication could not be used as a specific predictor of consciousness.Next, we tested if decreased dynamics of connectivity patterns are a feature of diminished consciousness  by analyThe distribution of patterns of global brain communication across groups and their dynamics had two further implications: (1) the AWAKE subjects can be both in patterns specific for AWAKE and patterns that can also be seen in the other groups, and (2) the absolute number of transitions is not specific for AWAKE, while the ability to transition to certain patterns is. To better understand when these group-specific transitions happen, we considered L1 distances (the sum of the absolute difference) of single covariance matrices with respect to a given pattern. Since the resulting space had seven dimensions (one dimension for each distance of covariance matrix to one of the seven clusters), we visualized the data in a pairwise manner . This reThis observation was further supported by the transition probability matrix . The prop < 0.05, B = 0.31), the bilateral calcarine gyrus , and the bilateral fusiform gyrus , as well as networks of the frontal lobe: the bilateral dorsomedial prefrontal cortex , the bilateral dorsolateral prefrontal cortex 1 , and the bilateral dorsolateral prefrontal cortex 2  or by a state of low overall correlation (pattern 1). This finding could be translated to patients during unresponsive wakefulness and controls, where the same patterns reliably identified intact consciousness and were absent in UWS. One main difference between the model of general anesthesia and patients with UWS is certainly the altered brain anatomy. The UWS cohort, however, also included subjects with intact brain anatomy and only subcortical pathology. The findings were also applicable to these individuals. In addition, the ICA is largely independent of the brain anatomy and was able to identify brain networks outside of lesioned brain tissue. We regard this relative independence of the data analysis pipeline from anatomical alterations as an advantage over atlas-based brain parcellations, allowing the future automatization of this approach. In summary, we hope that the direct identification of conscious information processing might be superior when compared with current state-of-the-art approaches using signal complexity  or machiBoth the models of general anesthesia and UWS showed reduced dynamics between patterns when compared with the AWAKE state. However, we encountered a relevant overlap between the different groups. This result demonstrates that the AWAKE state is much more characterized by continuous transitions between patterns specific and not specific for AWAKE than by the mere number of transitions. How such patterns arise in the brain has been investigated by computational models showing that specific patterns and their dynamics arise from the connectome of the human brain only if the brain works at a “critical” level , a tightThe activity within the dorsal prefrontal cortical areas, primary sensory networks, was also linked to the appearance conscious-related patterns of global brain communication. This observation is not only a mere reproduction of previous findings on the role of the dorsal attention system in consciousness  but alsoIn our study, the control cohort for the test data set consisted of healthy volunteers. The separation of the two groups is presumably easier than between a group of wakeful patients with severely damaged brains and our UWS group, a setting in clinical practice where this approach has to prove whether it actually delivers additional information to the clinical evaluation, i.e., is able to identify intact consciousness in patients with UWS in a real-world setting.In addition, the presented data analysis is very complex and not yet fully automatized. For an application in clinical routine, the analysis pipeline needs to be implemented in an automatized toolbox and made available to the general public.The limited number of subjects in each group is certainly another limiting factor. For the anesthesia experiments, it was already difficult to obtain ethics committee approval for a limited number of subjects. For the patients with UWS, it took several years to obtain sufficient data sets of adequate quality in this single-center study.These two major flaws should be addressed in a future study equipped with an easily available analysis tool and a comprehensive clinical study design.In summary, the findings of this study show that complex patterns of global brain communication are capable to separate consciousness from unconsciousness during both general anesthesia and disorders of consciousness. Thus, this approach might not only reveal intact consciousness in seemingly wakeful but unresponsive patients but also give an insight into the neurophysiological basis of consciousness itself.The raw data supporting the conclusions of this article will be made available by the authors, upon reasonable request.The studies involving human participants were reviewed and approved by Ethics committee of the Technical University Munich. The patients/participants provided their written informed consent to participate in this study.DG designed and conducted the experiments, conducted the data analysis, and wrote the manuscript. RW and JR conducted the experiments. AR, DJ, and RI designed and conducted the experiments and helped in writing the manuscript. GS helped in writing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The cumulative effect of mild traumatic brain injuries (mTBI) can result in chronic neurological damage, however the molecular mechanisms underpinning this detriment require further investigation. A closed head weight drop model that replicates the biomechanics and head acceleration forces of human mTBI was used to provide an exploration of the acute and chronic outcomes following single and repeated impacts. Adult male C57BL/6J mice were randomly assigned into one of four impact groups  which were delivered over 23 days. Outcomes were assessed 48 hours and 3 months following the final mTBI. Hippocampal spatial learning and memory assessment revealed impaired performance in the 15-impact group compared with control in the acute phase that persisted at chronic measurement. mRNA analyses were performed on brain tissue samples of the cortex and hippocampus using quantitative RT-PCR. Eight genes were assessed, namely MAPT, GFAP, AIF1, GRIA1, CCL11, TARDBP, TNF, and NEFL, with expression changes observed based on location and follow-up duration. The cortex and hippocampus showed vulnerability to insult, displaying upregulation of key excitotoxicity and inflammation genes. Serum samples showed no difference between groups for proteins phosphorylated tau and GFAP. These data suggest that the cumulative effect of the impacts was sufficient to induce mTBI pathophysiology and clinical features. The genes investigated in this study provide opportunity for further investigation of mTBI-related neuropathology and may provide targets in the development of therapies that help mitigate the effects of mTBI. Mild traumatic brain injuries (mTBI) are the most common form of closed head injury  and may There are concurrent and self-exacerbating processes that are triggered in response to mTBI. Physical and chemical damage lead to synaptic influx and inhibited reuptake of neurotransmitters that leads to calcium dysregulation in the process known as excitotoxicity . This reAnimal models are a common method for investigating the outcomes of head trauma . To inveThis study aimed to examine the cognitive, biochemical, and molecular changes resulting from repetitive mTBI in mice at very low impact thresholds. Three different impact totals were used to assess the possibility of a dose-dependent relationship with pathology and cognition. Behavioural changes were investigated with the use of tests previously demonstrated to evaluate acute and chronic mTBI symptoms involving neurological function and spatial learning and memory. mRNA expression changes in hippocampus and cerebral cortex were examined for neuronal damage with tau protein (MAPT), TDP-43 (TARDBP), and neurofilament light (NEFL); glial response with glial fibrillary acidic protein (GFAP) and allograft inflammatory factor 1(AIF1); excitotoxicity with glutamate ionotropic receptor AMPA type subunit 1 (GRIA1); and inflammation with C-C motif chemokine ligand 11 (CCL11) and tumour necrosis factor (TNF)  under guidelines from the National Medical Research Council of Australia. The ARRIVE guidelines were adhered to for the design and reporting of the study. A total of 64 male C57BL/6J mice  were housed in a constant 12:12 hour light-darkness cycle, with the temperature controlled at 22 ± 2°C. Mice were housed four to six per cage, and food and water access as permitted There were two separate study arms: an acute branch of animals sacrificed 48 hours following final impact, and a chronic branch of animals were sacrificed 90 days following final impact . Mice weTo mimic the head acceleration forces sustained in human mTBI, mice were subjected to mTBI via an apparatus designed and built for this purpose, as previously described . An enclTo assess neurological restoration following mTBI, time to recover righting reflex (RR) was monitored following isoflurane-induced anaesthesia (controls) or mTBI (n = 8 per group). Mice were placed in a supine position on the recovery pad, and the time taken for the animal to adopt a prone position was recorded after each impact or anaesthetic administration. RR time was calculated from the discontinuation of isoflurane inhalation to the first sign of righting.Neurological impairment of mTBI mice compared with controls was assessed via neurological severity score (NSS), which is a composite measure of motor function, alertness and behaviour in rodent models of TBI . NSS conControl and mTBI groups were tested in the Morris water maze (MWM), which provides a measure of hippocampal-dependent spatial learning and memory . The tes2, 5% CO2) artificial cerebrospinal fluid (CSF) containing 118.0 mM NaCl, 3.5 mM KCl, 1.3 mM MgCl2, 26.2 mM NaHCO3, 1.0 mM NaH2PO4, 2.5 mM CaCl2, 11.0 mM glucose, before being rapidly dissected on a frozen dissection platform for hippocampus and cerebral cortex sections. Sections were frozen at -80°C for genetic and biochemical analysis. To enable blinding conditions, collection tubes were coded so that group names were not accessible to the investigators undertaking sample analysis. Coding information was secured on the lead investigators computer, and the codes were only accessed after the samples were analysed.On PID 2 and 90, euthanasia was administered via inhalation of isoflurane between 4 to 6 minutes, with death confirmed by cessation of breathing. Blood samples were collected via the inferior vena cava and allowed to clot before being centrifuged at 5000 rpm for 15 minutes. Sera was aliquoted and stored at -80°C. The brain was removed and weighed, then washed in ice cold oxygenated  using the phenol-chloroform method . Sample Serum tau phosphorylated at threonine 231 (p-tau 231) and GFAP protein levels were qualified by ELISA kits  following the manufacturer’s instructions . All standards, positive and negative controls, and samples were run in duplicate. 96-well immunoplates  were coated with 100 μl of capture antibody and incubated overnight at 4°C. Non-specific binding was blocked with blocking buffer. 100 μl of samples and standards were then added to the coated wells for 1 hr at room temperature. After incubation, 100 μl of the working biotinylated detection antibody was added to each well and incubated for a further 1 hr. 100 μl of streptavidin-HRP was added to each well and incubated for 30 mins at room temperature. 3,3′,5,5′-tetramethylbenzidine was added to start the colour reaction. The reaction was stopped after 10 min with 1 M HCl solution, and the absorbency was immediately measured at 450 nm . All samples fell within normal range of the standard curve, which was 6.25pg/ml to 200pg/ml for p-tau and 62.5 to 4000 pg/ml for GFAP.Group numbers for behavioural and laboratory tests were calculated via an a priori power analysis using α of 0.05, power of 0.8, and means and SD from previously laboratory pilot data. Statistical analysis were performed using IBM SPSS Statistics for Windows Version 25.0 . Data were evaluated for normality (Shapiro-Wilks test or Kolomogov-Smirnov test) prior to statistical testing. As all data was parametric, 1-way ANOVA, or repeated-measures 2-way ANOVA, with Tukey post-hoc tests  was used to assess for statistically significant differences. All data are presented as means with standard deviations.Mice subjected to mTBIs in the 1-IMP, 5-IMP and 15-IMP groups showed no signs of convulsions or physical stress following impacts, indicating that our model sufficiently mimicked the mild impact forces typically seen in sub-concussive injury. Due to the asymptomatic nature of the injuries, no mice were withdrawn from the study on ethical grounds.p <0.05). RR latency in the 5-IMP group was significantly increased compared to control throughout the entire impact schedule (p <0.05). The increased latency to recover the righting reflex persisted in the 15-IMP group for impacts 1–6, compared to control (p <0.05). For impacts 7 through 15, recovery times were not significantly different in the 15-IMP group from controls (p <0.05). The 15-IMP group RR time was significantly greater than 5-IMP group following impacts 2 through 4, but not for impacts 1 and 5 (p <0.05). The 1-IMP RR time was not significantly different to control.Impact caused a significant increase in the time required to regain consciousness . Comparep = 0.2), and none of the mice were excluded from MWM testing based on lack of motor function. Mice from the control and impact groups showed progressive improvement in the ability to locate the hidden platform with each subsequent test. For acutely tested mice, mTBI groups displayed latency in this ability, and post-hoc analyses found that the 15-IMP group was statistically different to control at trial 2, 3, and 4 (p <0.05). There was no significant difference between 15-IMP and control at trial 1, and no differences between 15-IMP and other impact groups at any trial. No differences were seen from control with the 1-IMP and 5-IMP groups at any trial. At the chronic testing time-point, again only the 15-IMP group displayed significantly impaired ability to find the platform at trial 4 (p <0.05). There were no differences with the 15-IMP group compared to control at trial 1 through 3, and no differences between 15-IMP and the other impact groups. The 1-IMP and 5-IMP group times to find the platform were not different from control at any trial.Hippocampus-dependent spatial learning and memory was assessed at acute  and chro(p <0.05) and compared to 1-IMP (p <0.05)  .p <0.05). For chronic testing at PID 89, all mTBI groups revealed no significant differences in score for any group compared to sham control (p >0.05).In NSS  testing (p >0.05).There were no significant differences in bodyweight between impacted and non-impacted groups at all time-points. Bodyweight (mean ± SD) at the time of the euthanasia for acute mice was 23.26 ± 1.28 g, and 28.67 ± 2.09 g for chronic mice. The average brain weight was 0.43 ± 0.02 g, and there were no significant differences between any of the groups for brain weight p > 0.05). In the chronic cortex AIF1, CCL11 and TARDBP levels in the 15-IMP group were elevated relative to control (p > 0.05). In the hippocampus, acute measurement saw the increase of GRIA1, CCL11, and NEFL in the 15-IMP group (p > 0.05), while chronic measurement resulted in elevated GFAP levels in the 15-IMP group and elevated AIF1 in all impact groups relative to control (p > 0.05). Results pertaining to cortex and hippocampal mRNA expression change for acute and chronic mice can be seen in Due to the large amount of data generated from the molecular analysis, involving four groups, eight genes, two tissue types, and two time points, results information has been condensed into tables that contain all F and p values for the ANOVAs with corresponding post-hoc testing where required Tables  and 2. Gp >0.05).For groups measured at both 48 hours and 90 days after final mTBI there were no differences between any group in serum p-tau  or GFAP This study used a mouse model of mTBI that closely mimics the acceleration forces, impact speeds, and biomechanical properties of head impacts in humans. The impacts were delivered to the surgically unaltered head, and the parameters chosen were at the lowest impact weight reported . The impNeurological restoration, as measured by restoration of RR, was initially impaired in the 15-IMP group, as effects appeared to accumulate for the first 4 impacts. Interestingly, a sharp improvement was seen for impact 5, which persisted throughout the remainder of the impacts and resulted in neurological restoration times not different from the control group. This was despite consistent force of impact and did not coincide with the two-day ‘rest period’ of the impact schedule. It is unlikely that anaesthesia tolerance is a factor in this response, as the sham control receiving only anaesthesia did not exhibit a similar reduction in RR time. This phenomenon has been observed in previous studies, and these authors have hypothesised that a decrease in RR time may be as a result of CNS adaptation and initiation of neuroprotective pathways in response to repeated mTBI . The exaFindings from the behavioural data include differences in NSS in the acute phase of recovery 24 hours after final injury, but no differences between groups in the NSS at three months. Previous studies using a similar acceleration mTBI mechanism have shown NSS detriment at 1 hour through 7 days , and 24 Repetitive mTBI groups displayed spatial learning and memory deficits in acute MWM and probe trials that persisted in chronic testing. Differences between groups became more pronounced throughout the trial schedule, with the control group achieving maximum speed in finding the platform at trial 3, and the IMP-5 and IMP-15 groups displaying ongoing learning deficits until the final trial attempt. In the probe trial, the groups that received repetitive mTBIs spent significantly less time in the target quadrant searching for the platform. This is in line with previous studies , 35 thatThe purpose of this paper was not to identify a predictive and reliable biomarker of mTBI, but to provide additional details regarding the signalling processes within the brain following varying levels of injury. This study had two key goals: (1) to ensure that the subtle level of repetitive impact in our model was enough to induce mRNA expression changes in genes that have been associated with brain injury, and (2) that it allowed the investigation of novel genes that allow further understanding of pathways of pathology. Although there are numerous genes we could have analysed, we selected specific genes for investigation based upon the role they may play in the excitotoxicity, inflammation, and neurodegeneration in the context of mTBI. GRIA1 was assessed as a measure of excitotoxicity, which is a trigger of neurodegenerative cascades and is implicated in the disruption of spatial working memory in the hippocampus. To examine axonal neurodegeneration, MAPT and NEFL were selected, as they are indicators of structural damage and are involved in chronic neurofibrillary tangle formation and neurofilament breakdown, respectively. TARDBP was selected for its implication in these neurodegenerative processes and role in protein signalling and organelle transport within the neuron. GFAP and AIF1 were used as a measure of activated astrocytes and microglia, respectively. Glial cells are activated in response to injury where they promote neuroinflammation that is aimed at neurological recovery and repair. Finally, TNF and CCL11 were used as measures of inflammation, with TNF a classic pro-inflammatory cytokine that has been measured in TBI conditions, and increased CCL11 production found in brain aging and disease. As expected, the brain structure and the timing of analysis did influence the expression of the eight genes examined. Typically, for genes that were responsive to injury, a dose-dependent relationship was seen, whereby the highest increase in gene expression was seen in the 15-IMP group and the lowest increase in the single impact group.Excitotoxicity is an immediate consequence of mTBI, and involves the rapid synaptic influx and inhibited reuptake of neurotransmitters and amino acids which can result from mTBI . GlutamaMicrotubule associated protein-tau (MAPT) is specific to the axonal region of the neuron and is required in the organisation and construction of microtubule bundles . Under cNeurofilament light (NF-L) is a structural protein of myelinated axon cytoskeleton within white matter regions . NeurotrTDP-43 is involved in maintaining the expression of correct isoform ratios within the neuronal cytoskeleton . It is uGlial fibrillary acidic protein (GFAP) is a structural protein expressed by astrocytes , and is Microglia are the primary cells involved in regulating brain development and maintaining homeostasis through immune defence . In thesTNF is a cytokine that that has both pro and anti-inflammatory signalling properties, and is released from neurons, astrocytes and microglia after CNS insult , 77. TNFCCL11 is a chemokine that is released by microglia and astrocytes as part of the inflammatory response following TBI . Like otConsidering the information provided by the mRNA expression in the brain, we sought to examine if serum protein could be detected that demonstrated similar damage signalling. In human blood biomarker mTBI research, the most promising measure of neurological damage is tau, and astrocyte activation is GFAP . We measA limitation of this work is that protein levels of the genes of interest were not evaluated for the cortex and hippocampus. As the purpose of this study was to evaluate novel genes involved in known neurodegenerative pathways, protein-based pathology investigation was outside the scope of this work. As such, the preliminary findings described in this investigation should be expanded upon in further studies. Similarly, further details of the exact gene expression signalling mechanisms of inflammation and excitotoxicity (such as genes GRIA1 and CCL11) which are induced following repeated mTBI will need to be determined. It has been reported that the model used in this study is subject to greater variability in impact site than models using a stereotaxic device to secure the head . This maIn conclusion, this study provides further evidence in the role of repetitive subconcussive impacts in the vulnerability of the brain to injury and chronic neurodegeneration. This study used impact thresholds lower than previously reported in literature and confirmed behavioural detriment at acute testing that persisted to chronic impairment. Commonly assessed genetic markers were confirmed in this model, and inflammation and excitotoxic genes were implicated in the pathological cascade. In both behavioural and genetic data, there was evidence of a dose-dependent response where single impact had minimal effect, and the highest number of impacts resulted in neurological damage and decline. Nonetheless, there was no evidence of upregulation of serum proteins of disease at acute or chronic timepoints. Further investigations are needed to examine systemic protein circulation, and the presence of histological evidence of disease.This project was supported by the Australian Government Research Training Scheme. Funding to publish this study was provided by the Mackay Hospital and Health Service and the Mackay Institute of Research and Innovation. The funding sources was not involved in any of the following: study design; collection, analysis, and interpretation of data; writing of the report; or decision to submit the article for publication.S1 FigEdges have been defined based on co-expression, association in curated databases, or co-mentioned in publications.(TIF)Click here for additional data file. 12 Apr 2021PONE-D-20-37245Repetitive mild traumatic brain injury affects inflammation and excitotoxic mRNA expression at acute and chronic time-pointsPLOS ONEDear Dr. Hiskens,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. 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Please include your tables as part of your main manuscript and remove the individual files.Please note that supplementary tables should be uploaded as separate "supporting information" files.Additional Editor Comments:Dear Dr. Hiskens,the article was assessed by two experts in the area of TBI,. there is enthusiasm for this work and showed to carry scientific merit to he mTBI field, there are some minor comments pertaining to discussion and the selection of the genes. please check reviewer 1 and 2 for their commentsi would also suggest that an interaction map be constructed to show the connection among the altered genes.Looking forwad,FK[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: In the present manuscript titled “Repetitive mild traumatic brain injury affects inflammation and excitotoxic MRNA expression at acute and chronic time-points” authors Dr. Hiskens et al., presented a case study work where role of repetitive subconcussive Impacts were assessed by mRNA analyses performed on brain tissue samples of mTBI  mice, using quantitative RT-PCR. The main finding is, eight genes that were assessed  showed expression changes indicative of excitotoxicity and inflammation genes, that were observed on location and follow-up duration. However, there was no difference in serum samples between the groups for tau and GFAP proteins. Additionally, behavioral changes were investigated to evaluate acute and chronic mTBI symptoms involving neurological function and spatial learning and memory. The use of mTBI model of mice that mimics the subtler/milder form of TBI (as opposed to obvious injury) is a big positive in this this study. The time points (48 hrs to 3 months) taken in to consideration also adds a lot of value to the data. This together can provide value data to similar researchers in the field.The manuscript is written in a high quality style and the data generated supported the study hypothesis. The authors did a great job in communicating the results and the flow between each section is decent. The figures (including the schematic) was very helpful for the readers to understand the flow of the work. The statistical analysis was performed strategically . Same with the behavioral data presentation. The authors listed limitations of the study which I found was very observant.I recommend that this paper be accepted after minor questions below are answered.1. The kits used for ptau and GFAP: Are these kits ‘validated’ for use with mice samples? I suspect that the high background may be suppressing the signals . May be this is why there is no difference between mTBI VS control. Can I get a comment form the authors on this?2. Why did not the authors look at other proteins in the serum (ex: Neurofilament light (NfL) protein)? The reason being that this protein is being looked at various forms of concussion and also over pressure exposure. Can I get a comment form the authors on this?3. Did the author consider looking at CSF biochemical analysis for the proteins? Perhaps this may show some signals for the proteins of the study (as used in serum).Reviewer #2: The manuscript describes a rodent model of repetitive sub-concussive injuries and its effect on molecular and behavioral changes at acute and chronic time points following the injury. The objective of this project is interesting and an animal model that can replicate the effect of repetitive sub-concussive injuries in humans is needed. However, there are several issues with the experimental design and interpretation of results.1. What happens to the 25g weight at the impact. Is it controlled fall? If not, does the weight fall on the animal after the platform collapse? Is there any specific reason for using this model over the closed head CCI injury model which can be controlled relatively well with high reproducibility? How much the fall account for the head injury is not clear.2. It appears that the animals were not perfused before the collection of the brain. If that is the case then the gene expression analysis can be confounded by the presence of blood cells especially TNF alpha.3.Why did authors choose to evaluate these specific set of genes only. This is not clear.4.Figure 3: The latency times for the 15 IMP group were quite high during impact 1-6 than the 1-5 impacts of the 5 IMP group. The possible cause of this difference is not clear because essentially both the groups were treated in the same way until the first 5 impacts yet there is a significant difference?5. "15-IMP mice did not have performance differences compared with the 5-IMP group, and 1-IMPand 5-IMP mice performance were not different to control" This statement is not clear. If 15 impact and 5 impacts did not have performance difference. If 5 IMP is not different from sham control, then how is 15 impacts different than sham control?6. Was the PID 1 same for all the groups? In other words, did the authors conducted the NSS for all three groups together after the 15 impacts were over? if this is the case, then 1 IMP and the 5 IMP group would have a significant time for recovery compared to 15 IMP and it is likely that the difference in NSS is probably because of the test conducted immediately after the last injury and not necessarily because of the cumulative injury.7. The results are not explained in section 3.5 for the results. The key findings should be explained for the ease of readers.8. The lack of protein data reduced the significance of the molecular findings.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  23 Apr 2021Reviewer #1:1) The kits used for ptau and GFAP: Are these kits ‘validated’ for use with mice samples? I suspect that the high background may be suppressing the signals . May be this is why there is no difference between mTBI VS control. Can I get a comment form the authors on this?The ptau and GFAP kits are validated for use in quantifying mouse proteins in ELISA as per the manufacturer’s instructions. We suspect the reviewer is correct that the combination of high background and mild injury has resulted in the negative finding.2) Why did not the authors look at other proteins in the serum (ex: Neurofilament light (NfL) protein)? The reason being that this protein is being looked at various forms of concussion and also over pressure exposure. Can I get a comment form the authors on this?We agree with the reviewer that the investigation of Nfl protein would have been a valuable addition in this study, as this protein is widely investigated in mild TBI. We were limited to investigating two proteins due to the volume of serum that was collected in each subject.3) Did the author consider looking at CSF biochemical analysis for the proteins? Perhaps this may show some signals for the proteins of the study (as used in serum).We agree with the reviewer that CSF protein analysis would have been a valuable measure of protein change following injury. We included serum levels as these measures will be used in human mTBI for diagnosis. Our laboratory does not currently have the technical expertise to sample CSF from a mouse, however future studies using CSF protein analysis will be worth pursuing in understanding the relationship of expression across these fluids.Reviewer #2:1. What happens to the 25g weight at the impact. Is it controlled fall? If not, does the weight fall on the animal after the platform collapse? Is there any specific reason for using this model over the closed head CCI injury model which can be controlled relatively well with high reproducibility? How much the fall account for the head injury is not clear.The impact weight is tethered to the apparatus such that there is no unintended secondary impact can occur. This is explained in our methods section: “The impact weight was tethered to the guide tube by commercially available braided nylon line , restricting the fall of the weight so that it could continue downward no more than 1 cm beyond the starting position of the dorsal surface of the skull, thereby avoiding unintentional secondary contact.”https://doi.org/10.1002/jnr.24472 identified this model previously described https://doi.org/10.1016/j.ajpath.2016.07.013 as ideal for mimicking the acceleration forces, impact speeds and biomechanical properties of human mild TBI while avoiding the need for surgical alteration to the head.A systematic review that we published in 2019 We certainly agree with Reviewer #2 that CCI is useful in single impact moderate-severe forms of TBI, however this method is not appropriate for repetitive TBI due to the opening and closing of the skull.https://doi.org/10.1016/j.ajpath.2016.07.013The fall following head impact is a short distance (10cm) onto a cushion, and the lab that first described this method ensure no additional injury 2. It appears that the animals were not perfused before the collection of the brain. If that is the case then the gene expression analysis can be confounded by the presence of blood cells especially TNF alpha.We agree that perfusion is often ideal before the collection of the brain. However, we consider that the most important factor in our molecular analysis is protecting the integrity of the RNA in tissue by undertaking rapid dissection and ensuring that the tissue is as cold as possible.It is the perspective of our lab that because the perfusion procedure has a lag of a few minutes between cessation of effective circulation and cooling of the tissue, hypoxic and oxidative stress processes make endogenous RNases of the brain more active which could potentially result in transcriptional changes. For this reason, we do not perfuse before collecting tissue when the aim of the experiment is to analyse gene expression, instead we perform rapid dissection on an ice-cold surface with the tissue first rinsed and then bathed in artificial CSF.Reviewer #2 makes a very good justification for removing blood in the vasculature which may introduce peripheral inflammatory mediators expressing biological markers similar to microglia. However, we prefer to accept the blood as the natural state of the tissue and control for this constant across all animals because of the variation in effectiveness of perfusion between different animals. We feel that a reasonable assumption is that tissue dissected without perfusion contains the same amount of blood between biological replicates, versus potential differences in tissue that is perfused.An example of a recent publication from a different lab that uses the same technique of no perfusion in the mRNA expression of microglia is doi:10.1111/gbb.12736 3.Why did authors choose to evaluate these specific set of genes only. This is not clear.We thank Reviewer #2 for this point, there was a lack of clarity in our overview of decision to assess these genes. Although there are numerous genes we could have analysed, we selected specific genes for investigation based upon the role they may play in the specific secondary injury processes involved following mTBI, namely excitotoxicity, inflammation, and neurodegeneration.The discussion has been reordered to allow for a more logical flow of information regarding genes. Additionally, the following information has been added to the discussion of the manuscript:“GRIA1 was assessed as a measure of excitotoxicity, which is a trigger of neurodegenerative cascades and is implicated in the disruption of spatial working memory in the hippocampus. To examine axonal neurodegeneration, MAPT and NEFL were selected, as they are indicators of structural damage and are involved in chronic neurofibrillary tangle formation and neurofilament breakdown, respectively. TARDBP was selected for its implication in these neurodegenerative processes and role in protein signalling and organelle transport within the neuron. GFAP and AIF1 were used as a measure of activated astrocytes and microglia, respectively. Glial cells are activated in response to injury where they promote neuroinflammation that is aimed at neurological recovery and repair. Finally, TNF and CCL11 were used as measures of inflammation, with TNF a classic pro-inflammatory cytokine that has been measured in TBI conditions, and increased CCL11 production found in brain aging and disease.”Additionally, a module map has been constructed to show the connection among the altered genes. This has been included as S1 fig.4.Figure 3: The latency times for the 15 IMP group were quite high during impact 1-6 than the 1-5 impacts of the 5 IMP group. The possible cause of this difference is not clear because essentially both the groups were treated in the same way until the first 5 impacts yet there is a significant difference?This is an astute observation by the reviewer, in line with the results that we reported on these groups “The 15-IMP group RR time was significantly greater than 5-IMP group following impacts 2 through 4, but not for impacts 1 and 5“. Our explanation of this centres on the schedule of the impacts for each group, which saw the 5-IMP group receive 2 days of impacts, followed by 2 days of no impacts, before the final 3 days of impacts. In contrast, the 15-IMP group received 3 days of impacts, followed by 2 days with no impacts, before receiving the next set of 3 impacts. In this way it seems that the earlier opportunity for non-impact days had protective effect on the latency times when impacts were recommenced. In the discussion we explained this difference: ‘While the 5-IMP group had RR times significantly higher than control, this group did not see the same peak as the 15-IMP group, suggesting that the additional time between impacts served as an opportunity for healing such that accumulated detriment was not seen.’ Additional clarification of this point is now made in the discussion.5. "15-IMP mice did not have performance differences compared with the 5-IMP group, and 1-IMP and 5-IMP mice performance were not different to control" This statement is not clear. If 15 impact and 5 impacts did not have performance difference. If 5 IMP is not different from sham control, then how is 15 impacts different than sham control?Thank you for picking up the lack of clarity in this statement. We believe these details provide minimal value to the presentation of results, and the sentence has been removed.6. Was the PID 1 same for all the groups? In other words, did the authors conducted the NSS for all three groups together after the 15 impacts were over? if this is the case, then 1 IMP and the 5 IMP group would have a significant time for recovery compared to 15 IMP and it is likely that the difference in NSS is probably because of the test conducted immediately after the last injury and not necessarily because of the cumulative injuryFigure 2a shows the schedule of impacts for each of the groups across the 23 days of impact administration. The timing of the final impact for each group was the 23rd day of the study. The 1-IMP and 5-IMP groups were anesthetised but not impacted (designated on the figure by ‘A’) to match the timing of the impacts in the 15-IMP group. In this way, PID 1 was the first day after the final impact for every group – said another way, the NSS was administered 24 hrs after the final impact for every group.7. The results are not explained in section 3.5 for the results. The key findings should be explained for the ease of readers.The key findings have now been included in section 3.5:In the acute cortex the 15-IMP group showed increased expression of MAPT, GFAP, and TNF genes relative to control (p >0.05). In the chronic cortex AIF1, CCL11 and TARDBP levels in the 15-IMP group were elevated relative to control (p >0.05). In the hippocampus, acute measurement saw the increase of GRIA1, CCL11, and NEFL in the 15-IMP group (p >0.05), while chronic measurement resulted in elevated GFAP levels in the 15-IMP group and elevated AIF1 in all impact groups relative to control (p >0.05).8. The lack of protein data reduced the significance of the molecular findings.We agree with the reviewer that the lack of protein data reduces the significance of the molecular findings. A statement of this limitation is present in the discussion: “A limitation of this work is that protein levels of the genes of interest were not evaluated for the cortex and hippocampus. As the purpose of this study was to evaluate novel genes involved in known neurodegenerative pathways, protein-based pathology investigation was outside the scope of this work. As such, the preliminary findings described in this investigation should be expanded upon in further studies.”AttachmentResponse to reviewers.docxSubmitted filename: Click here for additional data file. 26 Apr 2021Repetitive mild traumatic brain injury affects inflammation and excitotoxic mRNA expression at acute and chronic time-pointsPONE-D-20-37245R1Dear Dr. Hiskens,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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For more information, please contact Kind regards,Firas H Kobeissy, PhDAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 28 Apr 2021PONE-D-20-37245R1 Repetitive mild traumatic brain injury affects inflammation and excitotoxic mRNA expression at acute and chronic time-points Dear Dr. Hiskens:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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Approximately two-thirds of patients with major depressive disorder do not achieve remission during their first treatment. There has been increasing interest in the use of digital, artificial intelligence–powered clinical decision support systems (CDSSs) to assist physicians in their treatment selection and management, improving the personalization and use of best practices such as measurement-based care. Previous literature shows that for digital mental health tools to be successful, the tool must be easy for patients and physicians to use and feasible within existing clinical workflows.This study aims to examine the feasibility of an artificial intelligence–powered CDSS, which combines the operationalized 2016 Canadian Network for Mood and Anxiety Treatments guidelines with a neural network–based individualized treatment remission prediction.Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria. Patients completed a minimum of one visit without the CDSS (baseline) and 2 subsequent visits where the CDSS was used by the physician (visits 1 and 2). The primary outcome of interest was change in appointment length after the introduction of the CDSS as a proxy for feasibility. Feasibility and acceptability data were collected through self-report questionnaires and semistructured interviews.Owing to the COVID-19 pandemic, the study was adapted to be completed entirely remotely. A total of 7 physicians recruited outpatients diagnosed with major depressive disorder according to the F2,24=0.805; mean squared error 58.08; P=.46). In total, 92% (12/13) of patients and 71% (5/7) of physicians felt that the tool was easy to use; 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS. Of the 13 patients, 6 (46%) felt that the patient-clinician relationship significantly or somewhat improved, whereas 7 (54%) felt that it did not change.Data were collected between January and November 2020. A total of 17 patients were enrolled in the study; of the 17 patients, 14 (82%) completed the study. There was no significant difference in appointment length between visits (introduction of the tool did not increase appointment length; Our findings confirm that the integration of the tool does not significantly increase appointment length and suggest that the CDSS is easy to use and may have positive effects on the patient-physician relationship for some patients. The CDSS is feasible and ready for effectiveness studies.ClinicalTrials.gov NCT04061642; http://clinicaltrials.gov/ct2/show/NCT04061642 Clinical decision support systems (CDSSs) consolidate large quantities of clinical information to provide clinicians with the necessary data to support medical decision-making and assist with managing treatment protocols -3. An emFeasibility and ease of use are major concerns as they roughly equate to the tolerability of drug treatment; with similar impact—much like a medication—a digital tool can only have a positive impact if patients  use it and continue to use it. A recent meta-analysis of randomized controlled trials aimed to establish the dropout rates of studies on medical smartphone apps tracking depressive symptoms . The anaOnly one-third of patients with depression who receive treatment will achieve remission during the first treatment course; most experience multiple treatment trials before entering remission . CliniciAifred is a CDSS that uses AI to assist clinicians in selecting treatments for major depressive disorder (MDD). The tool incorporates a deep-learning model that was validated and trained on clinical and demographic baseline data to support treatment selection by providing individualized probabilities of remission for specific treatment options. Please see the study by Benrimoh et al  for a deProviding patients with the ability to monitor their own mental health symptoms has attracted a great deal of interest across all ages . PatientFollowing a previous simulation center study  and aheaTo assess the feasibility of the CDSS for use in clinical practiceTo assess physician and patient trust in the CDSS and its effect on the clinician-patient relationshipTo assess the usability of the CDSS and study software and to ensure that major limitations are identified and rectified before clinical trialsTo assess engagement with the appOne key metric brought up by clinicians interviewed during initial stakeholder conversations was appointment length; clinicians are increasingly required to interact with time-consuming digital systems, and the fear of yet another system adding time to assessments is a reasonable one . We aim The study was approved by the research ethics board of the Douglas Mental Health University Institute (identifier: NCT04061642). All participants provided written informed consent to participate. The study was conducted according to the ethical principles stated in the Tri-Council Policy Statement on the Ethical Conduct for Research Involving Humans .This was a single-arm, naturalistic follow-up study aimed at assessing software usability and acceptability conducted between January and November 2020. This study was not designed to assess the clinical effectiveness of the tool, which will be the focus of an upcoming clinical trial. It is important to note that physicians were provided access to the tool but were free to use the tool and its AI predictions or ignore it.The study sample included 2 population groups: (1) physicians, including family physicians and psychiatrists and (2) patients of these physicians. The recruitment target was 10 physicians and 3-4 patients per physician .Physicians were recruited via recruitment email and direct contact by the study personnel. The sites consisted of university hospitals, primary care clinics, and psychiatric clinics in the Canadian province of Québec. Eligible physicians were family physicians or psychiatrists treating patients with depression on at least a monthly basis. Physicians who met the eligibility criteria were then invited to attend an introductory session with study personnel where the study and the AI model were described, and training on how to use the tool was administered.Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria . Of. Of39]. Of the 17 patients, 2 (12%) experienced side effects, as recorded on the Udvalg for Kliniske Undersøgelser Side Effects Rating Scale across the course of the study .Patients (n=14) were in the study for an average of 13.2 weeks or 92.4 days (SD 9.74 weeks or SD 68.18 days), excluding 2 patients who dropped out of the study within 1 week of creating their accounts. The mean time between the baseline appointment and visit 1 was 40.86 days (SD 29.40), and the mean time between visit 1 and visit 2 was 51.57 days (SD 62.58). For the 13 patients for whom the appointment length analysis was carried out, baseline visits lasted a mean of 19.53 minutes (SD 6.09). Visit 1 and visit 2 lasted a mean of 17.69 minutes (SD 10.12) and 21.48 minutes (SD 10.69), respectively . Our finWith regard to the subjective physician view of appointment length, of the 7 physicians, 4 (57%) rated that using the tool “took about the same time as my usual practice,” indicated by a rating of 3 on a 5-point Likert scale. In addition, of 13 patients, 8 (62%) felt that their appointment time did not change, whereas 1 (8%) patient felt that it decreased.With regard to the tool’s trustworthiness, 62% (8/13) of patients and 71% (5/7) of physicians rated that they trusted the CDSS, indicated by a 4 or 5 on a 5-point Likert scale. The mean STAR-P and STAR-C scores were 33.62 (SD 2.90) and 31.14 (SD 2.63) comparable with 38.4 (SD 12.0) and 31.5 (SD 6.9) in the original Scale to Assess Therapeutic Relationships in Community Mental Health Care study , respectGood overall usability of the CDSS, indicated by a 4 or 5 on a 5-point Likert scale, was rated by 92% (12/13) of patients and 71% (5/7) of physicians  physicians logged into the tool on the same day as the visit, and the clinical algorithm module  portion of the tool was accessed on 93% (13/14) of appointment days. At the subsequent visit 2 appointments, once again, all  physicians logged into the tool, whereas the clinical algorithm component was again accessed at 93% (13/14) of appointments.The mean completion rate of all PHQ-9 questionnaires sent from account creation to week 12 of the study was 64% (SD 28%). The mean completion rate of the PHQ-9 by patients alone in this time frame was 59% (SD 28%). The GAD-7 had a mean completion rate of 60% (SD 28%) in the first 12 weeks of the study. Completion rates for other time frames can be found in Of the 14 participants, 10 (71%) had mean PHQ-9 and GAD-7 completion rates ≥50%. The lowest completion rate among the patients for both questionnaires sent in the first 12 weeks was 8%. Most patients regularly completed questionnaires, with 76% (11/14) completing ≥33% of app assessments and 71% (10/14) completing ≥50% of the PHQ-9 and GAD-7 assessments in the app over the first 12 weeks in the study.Biweekly peaks in the completion rates were observed for the PHQ-9 and GAD-7 questionnaires  and 4. Ar12=–0.69; P=.006).In addition, an exploratory correlation analysis aimed at determining potential correlates of questionnaire completion rates revealed that patients who had appointments scheduled further apart were less likely to complete the PHQ-9  of the physicians felt that using the tool in session took approximately the same time as their usual practice. A systematic literature review conducted by Kerst et al  found thWe found that 62% (8/13) of patients endorsed some degree of trust in the CDSS, somewhat lower than the percentage of clinicians  with some degree of trust. This may have in part been because of COVID-19: most clinicians followed up with their patients by phone, which meant that patients did not get to view the AI results on the physician’s screen as intended, which may have improved feelings of trust had it occurred more frequently; standardized patients noted that looking at the screen with their physicians was a positive experience in our previous simulation center study . NonetheThe main limitation of the study is the small sample size, which limits the strength of the conclusions that can be drawn from this study. All results should be considered preliminary and are only intended to demonstrate feasibility; similar metrics of feasibility and ease of use will be used during our upcoming clinical trial to confirm and expand upon these findings. Another weakness is the heterogeneity in the severity of the patients’ depression, which limits our ability to comment on tool effectiveness and generalize these results to different strata of illness severity. Nevertheless, it also presents as a strength because it allowed us to demonstrate feasibility in a range of clinical situations.Our study fell short of recruitment targets, which may have been in part because of several scenarios. One possibility is that there were sufficient patients available to recruit but that these patients were not interested in participating in the study. However, this is not supported by the high proportion of approached patients who agreed to participate. Furthermore, of the patients who did participate, dropout rates were comparable with other studies of mental health applications, as reviewed in the introduction. Another more likely scenario is that there were fewer patients available to recruit than expected; this seems to have been the case because of the COVID-19 pandemic. As discussed, 3 of our physicians were unable to recruit patients because their practices were significantly changed by the pandemic, and they either did not follow patients longitudinally or had very few new patients who were eligible to participate. In addition, physicians who successfully recruited patients frequently commented on the lack of patients presenting with MDD . As such, the low sample size in this study is not indicative of patient interest in participation but rather of the number of patients available to be recruited during the pandemic. We noted that in unpublished data from a quality improvement project, which included a control group using a version of the CDSS without the AI enabled , which was initiated later in the pandemic , recruitment speed was much faster, with 34 patients recruited in 7 months, indicating that recruitment should not be a barrier for our upcoming clinical trial.However, the low sample size does mean that our study was underpowered to detect differences in appointment length within groups over time when considering an a priori sample size calculation for repeated measures as described above. Despite this, there are 2 reasons why this study provides useful information. First, the appointment lengths are interpretable data. Should appointment lengths have been different in the order of ≥5 minutes, which in previous work  has beenAn additional and significant limitation of this study is that although its design allowed us to examine the impact of introducing the tool on the patient-clinician relationship and the clinician workflow, this at the same time prevented us from examining the effectiveness of the device in terms of improvement in depression scores. This is because the tool being introduced well into a patient’s treatment course could not have its intended effect of assisting treatment selection or helping clinicians implement measurement-based care and algorithm-guided treatment across the entire length of the study. This was compounded by the delays between appointments and the reduced number of visits as a result of COVID-19. However, we note that the decision was made during study design not to focus on effectiveness and, because of the novelty of the device and the need to determine challenges to its introduction into clinical practice, to focus squarely on feasibility. As such, the modest improvements in depression and anxiety scores seen here are in line with expectations, given that the tool was not introduced in a manner where it could have its intended effect on patient care.With regard to feasibility, ease of use, and the ability to correct any major limitations, it would be reasonable to be concerned about whether this study, with its small sample size, could on its own speak to the readiness of the tool for clinical trials. However, this study should be considered in the context of previously published evidence on the same tool, which demonstrated its ease of use in a simulation center environment . In addiIn this paper, we have demonstrated that the Aifred CDSS is feasible and easy for clinicians and patients to use in a longitudinal manner and that it does not require increased time to use in clinic. In addition, the tool had an interesting impact on the clinician-patient relationship. For roughly half of the patients, it did not negatively or positively affect the relationship, helping to allay concerns about technological solutions worsening relationships between clinicians and patients. For the other half of the patients, the relationship was actually rated as having improved, indicating that for some patients, the CDSS may have beneficial effects on the clinician-patient relationship. This latter point will be further elaborated in a future study and should be investigated in future work. Planned clinical trials will serve as an opportunity to confirm these feasibility results and to determine if the CDSS is effective in improving depression outcomes.