The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype (‘inflammation/ageing’) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdalar atrophy; this subtype had highest anhedonia burden. A distinct ‘inflammation/energy dysregulation’ group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the ‘inflammation-resilient’ group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions. ### Competing Interest Statement The authors have declared no competing interest. National Key R&D Program of China, 2018YFC1312904, 2019YFA0709502 Shanghai Municipal Science and Technology Major Project, 2018SHZDZX01 China Postdoctoral Science Foundation, 2023M740683 National Natural Science Foundation of China, 32500995 Science and Technology Innovation 2030 - Major Projects

Magnetic resonance imaging (MRI) offers superior diagnostic quality but suffers from prolonged acquisition times, leading to patient discomfort and motion artifacts. The challenge of undersampled MRI data adversely impacts brain tumor classification accuracy. To address this, we propose D2EF-Net, a unified framework for brain tumor classification from undersampled MRI data. The model integrates MRI reconstruction and classification into a joint learning framework, preserving key diagnostic features while improving accuracy. D2EF-Net introduces three novel modules: adaptive multiscale convolution (AMC) for efficient feature extraction, residual depthwise convolution (RDC) for reduced complexity, and attention-enhanced hybrid transformer (AHT) for comprehensive feature representation. Extensive experiments on five datasets (DS-1 to DS-5) demonstrate that D2EF-Net significantly outperforms existing methods in tumor classification accuracy. Notably, it achieved average improvements of 4.66%, 4.61 %, 14.94%, 10.01%, 28.53%, 10.07%, 10.82%, 4.60%, 26.01 %, and 7.97% over baseline models. Additionally, D2EF-Net excels in fully-sampled data scenarios, further showcasing the flexibility of its joint learning mechanism. In conclusion, D2EF-Net offers a robust solution for accelerating MRI acquisition while maintaining high diagnostic accuracy, with potential applications in clinical practice.

Background Formal Thought Disorder and includes both positive (i.e., disorganized speech) and negative (i.e., impoverished speech) symptoms. Emerging evidence suggests that the cerebellum plays a critical role in cognitive functions, including language processing. This study leverages Natural Language Processing to objectively measure language disturbances in patients with first-episode psychosis and investigates the relationship between these disturbances and cerebellar structure. Methods Fifty-four patients with schizophrenia, either drug-naïve or minimally medicated, were recruited from an early psychosis program. Impoverished thought was assessed using the Thought Language Index while lexico-semantic features (affect, cognitive, linguistic, perception, time) were identified from speech samples analyzed using the Linguistic Inquiry Word Count-22 software. Structural cerebellar analysis was completed on 7.0 Tesla MRI scans using voxel-based morphometry (VBM) to measure global and regional grey matter volume changes. Results Linear regression analysis revealed that reduced perceptual word usage was the strongest predictor of impoverished thinking. Correlational analysis identified reduced cerebellar volumes in patients with lower LIWC-based perception scores. VBM localized this relationship to a cluster in the right posterolateral cerebellar hemisphere, an area related to executive demand and verb generation function. Conclusion The cerebellum contributes to impoverished thinking in early psychosis, likely by influencing the lexical expression of perceptual experiences. This underscores the cerebellum's role in higher-order cognitive processes relevant to psychotic disorders and its potential as a therapeutic target for language and cognitive deficits in schizophrenia.

Schizophrenia is characterized with greater variability beyond the mean differences in brain structures. This variability is assumed to be static, reflecting the presence of heterogeneous subgroups, but this assumption and alternative explanations remain untested. Here we examine whether gray matter volume variability decreases in later stages of schizophrenia using magnetic resonance imaging of 1,792 individuals with schizophrenia and 1,523 healthy controls. Compared with healthy controls, greater variability (false-discovery-rate-corrected P < 0.05) was found in 50 regions across the entire patient group. The average variability across all regions was greater in the first-episode than chronic stage (t = 10.8, P = 1.7 x 10(-7)). The areas with the largest variability were located at the frontotemporal cortex and thalamus (first-episode), or the hippocampus and caudate (chronic). This study offers novel insights into the diversity of brain alterations in schizophrenia, emphasizing that brain-based heterogeneity is not a static feature; it is more pronounced at the onset of the disorder but reduced over the long term.

Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders.

Importance: Schizophrenia is characterized with greater variability beyond the mean differences in brain structures. This variability is often assumed to be static, reflecting the presence of heterogeneous subgroups, but this assumption and alternative explanations remain untested. Objective: To test if gray matter volume (GMV) variability is more less in later stages of schizophrenia, and evaluate if a putative "spreading pattern" with GMV deficits originating in one part of the brain and diffusing elsewhere explain the variability of schizophrenia. Design, settings, and participants: This study evaluated the regional GMV variability using MRI of 1,792 individuals with schizophrenia and 1,523 healthy controls (HCs), and the association of GMV variability with neurotransmitter and transcriptomic gene data in the human brain. Main outcomes and measures: Regional variability was evaluated by comparing the relative variability of patients to controls, using the relative mean-scaled log variability ratio (lnCVR). A network diffusion model (NDM) was employed to simulate the possible processes of GMV alteration across brain regions. Results: Compared with HCs, greater lnCVR (pFDR<0.05) was found in 50 regions in the whole patient group (n=1792; 762 females; mean[SD] age, 29.9[11.9] years), at a much greater frequency (p=5.0x10-13) in the first-episode drug-naive subsample (73 regions) (n=478; mean[SD] illness duration, 0.548[0.459] years), compared to the chronic medicated subsample (28 regions) (n=398; mean[SD] illness duration, 14.0[10.4] years). The average lnCVR across all regions was greater in the first-episode than chronic subsample (t=10.8, p=1.7x10-7). The areas with largest lnCVR were located at frontotemporal cortex and thalamus (first-episode), or hippocampus and caudate (chronic); there was a significant correlation with case-control mean difference (r=0.367, p=6.7x10-4). We determined a gene expression map that correlated with the lnCVR map in schizophrenia (r=0.491, p=0.003). The NDM performed consistently (72.1% patients, pspin<0.001) in replicating GMV changes when simulated and observed values were compared. Conclusion and relevance: Brain-based heterogeneity is unlikely to be a static feature of schizophrenia; it is more pronounced at the onset of the disorder but reduced over the long term. Differences in the site of "origin" of GMV changes in individual-level may explain the observed anatomical variability in schizophrenia ### Competing Interest Statement LP reports personal fees for serving as chief editor from the Canadian Medical Association Journals, speaker/consultant fee from Janssen Canada and Otsuka Canada, SPMM Course Limited, UK, Canadian Psychiatric Association; book royalties from Oxford University Press; investigator-initiated educational grants from Janssen Canada, Sunovion and Otsuka Canada outside the submitted work. All other authors disclose no conflict of interest. ### Funding Statement This study did not receive any funding. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was conducted under the approval of the Medical Research Ethics Committee of Fudan University, Shanghai, China. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Artificial intelligence provides an opportunity to try to redefine disease subtypes based on similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) with cross-sectional MRI from 296 individuals with focal epilepsy originating from the temporal lobe (TLE) and 91 healthy controls, we show phenotypic heterogeneity in the pathophysiological progression of TLE. This study was registered in the Chinese Clinical Trials Registry (number: ChiCTR2200062562). We identify two hippocampus-predominant phenotypes, characterized by atrophy beginning in the left or right hippocampus; a third cortex-predominant phenotype, characterized by hippocampus atrophy after the neocortex; and a fourth phenotype without atrophy but amygdala enlargement. These four subtypes are replicated in the independent validation cohort (109 individuals). These subtypes show differences in neuroanatomical signature, disease progression and epilepsy characteristics. Five-year follow-up observations of these individuals reveal differential seizure outcomes among subtypes, indicating that specific subtypes may benefit from temporal surgery or pharmacological treatment. These findings suggest a diverse pathobiological basis underlying focal epilepsy that potentially yields to stratification and prognostication - a necessary step for precise medicine.

Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear. Objective This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression. Methods: This study included 107 PD patients (mean age: 60.06 +/- 9.98 years, 70.09% male) with baseline and >= 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes. Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part I, beta = 1.26 +/- 0.18, P = 0.016) and motor (MDS-UPDRS-Part II, beta = 1.34 +/- 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, beta = 1.15 +/- 0.22, P = 0.043), memory (HVLT-Retention, beta = -0.02 +/- 0.01, P = 0.016) and depression (GDS, beta = 0.26 +/- 0.083, P = 0.019) compared to subtype 1. Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.

We emphasise the existence of two distinct neurophysiological subtypes in schizophrenia, characterised by different sites of initial grey matter loss. We review evidence for potential neuromolecular mechanisms underlying these subtypes, proposing a biologically based disease classification approach to unify macro- and micro-scale neural abnormalities of schizophrenia.

Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

Major depressive disorder (MDD) has been characterized by structural abnormalities of multiple brain regions. Nevertheless, little is known about the underlying neuropathological origin of MDD, particularly based on distinct trajectories of brain atrophy. Here, using the data-driven subtype and stage inference algorithm on large case–control magnetic resonance imaging data from 3,940 individuals (1,789 patients with MDD; 2,151 healthy controls), we demonstrated three highly robust spatiotemporal MDD subtypes: subtype 1 initiates from the subgenual anterior cingulate cortex, subtype 2 starts at the hippocampus and subtype 3 begins in the superior frontal gyrus and then the orbitofrontal cortex. These subtypes also exhibited distinct clinical profiles and differing transcriptomic gene expressions. Specifically, we identified suicide risk as the characteristic symptom for the ‘anterior cingulate cortex-led’ subtype, as well as low motivation (for example, work interests) for the ‘frontal-led’ and somatic anxiety for the ‘hippocampus-led’. Distinguishable cell type-specific transcriptional signatures further indicate distinct origins of MDD subtypes. Together, our data-driven findings demonstrate different spatiotemporal trajectories of MDD subtypes, which may contribute to the potential for individualized diagnostics, suicide risk alerts and optimizing therapeutic targeting.

Temporal lobe epilepsy (TLE) is the most common type of intractable epilepsy in adults. Although brain myelination alterations have been observed in TLE, it remains unclear how the myelination network changes in TLE. This study developed a novel method in characterization of myelination structural covariance network (mSCN) by T1-weighted and T2-weighted magnetic resonance imaging (MRI). The mSCNs were estimated in 42 left TLE (LTLE), 42 right TLE (RTLE) patients, and 41 healthy controls (HCs). The topology of mSCN was analyzed by graph theory. Voxel-wise comparisons of myelination laterality were also examined among the three groups. Compared to HC, both patient groups showed decreased myelination in frontotemporal regions, amygdala, and thalamus; however, the LTLE showed lower myelination in left medial temporal regions than RTLE. Moreover, the LTLE exhibited decreased global efficiency compared with HC and more increased connections than RTLE. The laterality in putamen was differently altered between the two patient groups: higher laterality at posterior putamen in LTLE and higher laterality at anterior putamen in RTLE. The putamen may play a transfer station role in damage spreading induced by epileptic seizures from the hippocampus. This study provided a novel workflow by combination of T1-weighted and T2-weighted MRI to investigate in vivo the myelin-related microstructural feature in epileptic patients first time. Disconnections of mSCN implicate that TLE is a system disorder with widespread disruptions at regional and network levels.

BACKGROUND:The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS:We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS:We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS:Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca’s area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Objective To explore clinical and structural differences between mesial temporal lobe epilepsy (mTLE) patients with different hippocampal sclerosis (HS) subtypes. Methods High-resolution T1-weighted MRI and diffusion tensor imaging data were obtained in 41 refractory mTLE patients and 52 age- and sex-matched healthy controls. Postoperative histopathological examination confirmed HS type 1 in 30 patients and HS type 2 in eleven patients. Clinical features, postoperative seizure outcomes, hippocampal subfields volumes, fractional anisotropy (FA) values of white matter regions and graph theory parameters were explored and compared between the HS type 1 and HS type 2 groups. Results No significant differences in clinical features and postsurgical seizure outcomes were found between the HS type 1 and type 2 groups. However, the HS type 1 group showed extra atrophy in ipsilateral parasubiculum than healthy controls and more severe atrophy in contralateral hippocampal fissure than the HS type 2 group. More extensive FA decrease were also observed in the HS type 1 group, involving ipsilateral optic radiation, superior fronto-occipital fasciculus, contralateral uncinate fasciculus, tapetum, bilateral hippocampal cingulum, corona radiata, etc. Furthermore, in spite of similar impairments in characteristic path length, global efficiency and local efficiency in two HS groups, the HS type 1 group showed additional decrease of clustering coefficient than healthy controls. Conclusions HS type 1 and 2 groups had similar clinical characteristics and postoperative seizure outcomes. More widespread neuronal cell loss in the HS type 1 group contributed to more extensive structural damage and connectivity abnormality. These results shed new light on the imaging correlates of different HS pathology.

Technical developments and improved access to neuroimaging techniques have brought us closer to understanding the neuropathological origins of schizophrenia. Using data-driven disease-progression modelling on cross-sectional magnetic resonance imaging (MRI) from 1,124 patients with schizophrenia, we characterize two distinct but stable ‘trajectories’ of brain atrophy, separately beginning in the Broca’s area (subtype1) and the hippocampus (subtype2). The two trajectories are replicated in cross-validation samples. Individuals within each subtype are further classified into two stages (‘pre-atrophy’ and ‘post-atrophy’). These subtypes show different atrophy patterns and symptom profiles. Longitudinal data from 523 patients with schizophrenia treated by antipsychotics only or adjunct transcranial magnetic stimulation (TMS) reveal that antipsychotics-only effects relate to phenotypic subtype (more effective in the subtype1) while adjunct transcranial-magnetic-stimulation effects relate to the stage (superior outcomes in the pre-atrophy stage). These findings suggest distinct pathophysiological processes underlying schizophrenia that potentially yield to stratification and prognostication—a key requirement for personalizing treatments in enduring illnesses.