Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), yet variability of therapeutic responses remains high. A key contributor to this variability may be state-dependent effects of brain stimulation, where activity in underlying circuits can shape the propagation of TMS-evoked activity. Thus, pushing target circuits into a desired state by engaging cognition may sensitise those circuits to TMS-evoked plasticity. We tested whether TMS effects on cognitive control, a transdiagnostic construct implicated across psychiatric disorders and a putative mediator of TMS efficacy, are state-dependent. Participants (N = 25) completed a context-dependent behavioural assay of cognitive control before and after we delivered rTMS to the prefrontal cortex (PFC). During rTMS, participants performed either the cognitive control task (active-state TMS), or performed a context-independent perceptual task (control-state TMS). We assessed changes in downstream behavioural metrics of circuit function, as well as neural indices of cognitive control measured from electroencephalography (EEG). rTMS enhanced cognitive control performance only when PFC-anchored control circuits were actively engaged during stimulation. Similarly, only active-state TMS modulated theta band oscillatory activity, which is thought to be a marker of cognitive control engagement. Moreover, modulation of those EEG indices by TMS predicted gains in behavioural performance in the cognitive control task. These findings demonstrate that TMS effects on cognitive control are state-dependent, in which endogenous engagement of PFC-anchored networks can shape the magnitude and functional relevance of TMS-induced plasticity. Considering cognitive states during TMS may therefore offer a framework to enhance and/or accelerate TMS therapeutic effects.

BackgroundNormative modeling of brain development has gained traction for quantifying individual deviations in maturation. The brain age gap (BAG), the difference between predicted age from MRI features and chronological age, offers a potential individualized normative metric of neurodevelopment. However, consistent patterns across psychiatric disorders remain elusive, and no studies have examined whether BAG can predict developmental trajectories within an inclusive continuous model of youths cognition and behavior. MethodsUsing longitudinal data from the Adolescent Brain Cognitive Development Study (ages 9-15, n=9,074), we built 8 region-specific brain age models using volumes, thicknesses, and surface areas of parcels from the Brainnetome adolescent atlas. We derived psychiatric diagnoses from a parental questionnaire. Multivariate linear regression was used to assess case-control differences and cross-sectional continuous cognitive/behavioral profiles. We modeled cognitive/behavioral trajectories using a multivariate joint latent-class mixed model and assessed the relationship with BAG values using multinomial logistic regression. ResultsChildren with ADHD showed delayed maturation across multiple regions (Cohens d: - 0.12 to -0.08), while subcortical BAG emerged as a transdiagnostic indicator of delayed development (d: -0.07, pfdr = 0.024). Accelerated maturation characterized the high cognition and low symptom profile, while the inverse was found for the low cognition profile. Three developmental trajectories were identified: stable, towards externalizing behaviors, or internalizing behaviors. Widespread accelerated maturation predicted evolution towards internalizing behaviors but was protective against the externalizing trajectory. ConclusionsIntegrating BAG with continuous cognitive and behavioral profiles yielded a plausible framework for early identification of atypical trajectories, potentially contributing to personalized medicine in psychiatry.

BackgroundDiagnostic categories in psychiatry are based on symptoms and include individuals with different underlying pathology. This within-diagnosis heterogeneity confounds new treatment development and treatment selection for individual patients. A research priority is to discover biomarkers that define groups of patients with similar neuropathology. Digital neurocognitive therapy (DNT) and assessment can provide micro-cognition biomarkers of unprecedented precision. The brain is hierarchically organized from single cells to neurosystems that integrate action of millions of neurons across the brain necessary for cognition and emotion. Micro-cognition biomarkers identify dysfunction at the level of neurosystems that produce clinical illness. We used micro-cognition biomarkers to identify subgroups of children diagnosed with ADHD but with different neuropathology. MethodsK-means clustering was applied to 69 children 6-9 years old with ADHD using performance variables from a Go/NoGo test and the results analyzed against 58 typically developing (TD) children. Neurosystem dysfunction in each group was further characterized by micro-cognition biomarkers extracted from thousands of responses by each child during DNT. FindingsFour highly reproducible clusters were identified that differed on emblematic features of ADHD. Cluster 4 showed two core ADHD features, poor response inhibition and inconsistent attention. Cluster 3 showed only poor response inhibition and the other two showed neither. Cluster 2 showed faster and more consistent responses, higher detection of simple targets and better working memory than TD children but showed the most marked performance decrements when required to track multiple targets or ignore distractors. Cluster 1 showed much greater ability recognizing members of abstract categories rather than natural categories that children learn through physical interaction with the environment while Cluster 4 was the opposite. InterpretationDNT provides data-rich fine-grained, low-cost, noninvasive, and scalable micro-cognition biomarkers that characterize subgroups of patients with the same symptom-based psychiatric diagnosis but differing neuropathology.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder and is a risk factor for later brain disorders. Here, we characterize the relationship between ADHD status and white matter cellularity across development and examine associations with medication, using a novel biophysical diffusion MRI model in youth aged 9 to 14 years. Methods: The ABCD Study(R) is a longitudinal cohort study with three biennial waves of brain MRI collection. Twenty-seven white matter tracts were delineated using multi-shell diffusion-weighted imaging (DWI) and tractography. Intracellular isotropic (RNI) and directional (RND) diffusion were quantified using the Restriction Spectrum Imaging (RSI) model. Longitudinal linear mixed-effect models characterize the effects of ADHD status and medication use on white matter cellularity across three waves. Results: By wave: 9,426 participants at baseline (mean [SD] age: 9.92 [0.63] years; 48.7% Female; 12.2% with ADHD), 6745 participants at 2-year (11.95 [0.65] years; 46.8% Female; 11.3% with ADHD), and 2,483 participants at 4-year (14.07 [0.69] years; 46.0% Female; 11.8% with ADHD). ADHD was associated with decreased RNI in 20 tracts at age 9, with evidence of developmental trajectory differences suggesting attenuation over early adolescence. We found enduring ADHD-associated decreases in RND of 16 tracts spanning ages 9 to 14 years, with methylphenidate effects on 2 tracts. Low-motion sensitivity analyses confirmed robust RNI findings, but not RND findings. ConclusionsADHD was associated with reductions in isotropic diffusion in white matter tracts, and possibly with complementary reductions in directional diffusion of select tracts. Isotropic diffusion findings suggest atypical glial cellularity in white matter during late childhood.

Adolescent major depressive disorder (MDD) exhibits complex and heterogeneous alterations of brain functional organization. To understand the neurobiological basis of adolescent MDD, we adopted resting-state functional MRI data and used various matrix decomposition approaches to obtain the organization gradients, temporal dynamics, and information streams. With clustering sensory-association gradient features in our exploratory sample (NMDD = 250 and NControls = 203), we identified two MDD subtypes. Subtype 1 was characterized by sensory contraction and subtype 2 was associated with association expansion. In addition, two subtypes showed divergent bottom-up and top-down information flows in sensory and association areas using temporal dynamics analysis. These subtypes exhibit distinct age-related changes and reorganization trajectories along sensory-association and auditory-visual axes, highlighting that cortical information flow patterns systematically vary and relate differently to sensory integration, cognitive complexity, and aging. These network distinctions are linked to clinical severity and molecular mechanisms. Subtype 1 is predominantly associated with early neurodevelopmental abnormalities and emotional regulation deficits, while Subtype 2 is more related to synaptic dysfunction and reduced neuronal excitability. These results could be largely replicated in another independent sample (NMDD = 73 and NControls = 28). We therefore construct a sensory-association dual functional framework to characterize MDD heterogeneity in adolescent MDD. Itl integrates cortical hierarchy, developmental trajectories, and genetic influences, offering novel insights into MDD pathophysiology and providing a theoretical foundation for precision psychiatry, facilitating personalized diagnosis and intervention strategies.

Children with ADHD often exhibit fluctuations in attention and heightened reward sensitivity. Psychostimulants, such as methylphenidate (MPH), improve these behaviors in many, but not all, children with ADHD. Given the extent to which psychostimulants are prescribed for children, coupled with variable efficacy on an individual level, a better understanding of the mechanisms through which MPH changes brain function and behavior is necessary. MPHs primary action is on catecholamines, including dopamine and norepinephrine. Catecholaminergic signaling can influence the tradeoff between flexibility and stability of brain function, which is one candidate mechanism through which MPH may alter brain function and behavior. Time-varying functional connectivity, which models how functional brain networks reconfigure on short timescales, can be used to examine brain flexibility versus stability, and is thus well-suited to test how MPH impacts brain function. Here, we scanned stimulant-naive children with ADHD (8-12 years) on and off a single dose of MPH. In the MRI machine, participants completed two attention-demanding tasks: 1) a standard go/no-go task and 2) a rewarded go/no-go task. For both tasks, using a within-subjects design, we compared the degree to which brain organization changed throughout the course of the MRI scan, termed whole brain flexibility, on and off MPH. We found that whole brain flexibility decreased on MPH. Further, individuals with greater decreases in whole brain flexibility on MPH exhibited greater improvements in task performance. Together, these results provide novel insights into the neurobiological mechanisms underlying the effectiveness of MPH administration for children with ADHD.

BACKGROUNDReduced data-gathering and altered sensory precision are associated with psychotic phenotypes in tasks engaging the posterior parietal cortex (PPC). We investigated whether PPC excitability - modulated via 1 Hz repetitive transcranial magnetic stimulation (TMS) - differentially affects these behavioural patterns in high vs. low psychotic phenotypes. Based on prior work, we hypothesised that delusional and hallucinatory traits would moderate TMS effects on sensory precision (proxied by drift rates), while hallucinatory traits would additionally moderate effects on decision thresholds. METHODSWe compared performance in both the random dot motion task (RDM) and the beads task in two groups of participants (total, N = 68) undergoing TMS or sham-TMS over the right PPC. Hierarchical drift-diffusion models estimated drift rates (sensory precision proxies) and decision thresholds. We evaluated differences between TMS and sham-TMS groups and tested for interactions of these TMS groups with delusional and hallucinatory phenotypes. RESULTSIn RDM, TMS increased decision thresholds compared to sham-TMS in the low psychotic phenotype group. This effect was not present in participants with higher psychotic phenotypes. Drift rates, in contrast, were lowered in participants with higher delusional phenotype. No significant effect was found on beads task performance. CONCLUSIONSOur findings suggest a causal role of PPC in decisions to end data-gathering during perceptual inference. The absence of this effect in the psychotic phenotype yields new hypotheses on the role of PPC excitability in neural mechanisms underlying decision-making patterns in the psychotic phenotype.

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examined whether scalp electroencephalography (EEG) data features beyond spectral power, including waveform shape, broadband spectral structure, and signal complexity, provide complementary information for predicting cognitive and clinical outcomes. EEG was recorded from 200 adults spanning a broad range of neuropsychiatric symptom severity while they completed three WM task paradigms: Sternberg spatial WM (SWM), delayed face recognition (DFR), and dot pattern expectancy (DPX). Separate machine learning models were trained on EEG features from the encoding, delay, and probe phase of each task to predict participants task accuracy, reaction time (RT) variability, WM capacity, and psychopathology scores (Brief Psychiatric Rating Scale). A split-half analytic framework was used, with cross-validated model development in an exploratory dataset (N=100) and evaluation of statistically significant models in a held-out validation dataset (N=100). In the exploratory dataset, SWM task data best predicted WM capacity, DPX task data predicted RT variability, and DFR task data predicted psychopathology, suggesting that these three WM paradigms engage distinct neural processes relevant to different outcomes. No models reliably predicted task accuracy. Models incorporating features beyond spectral power generally outperformed power-only models, and task-derived features outperformed resting-state-derived features. However, only those models predicting WM capacity and RT variability generalized to the validation dataset; models predicting psychopathology did not. These findings demonstrate functional heterogeneity across WM paradigms, show that complementary EEG features enhance predictive modeling, and highlight the importance of rigorous validation for identifying robust brain-behavior relationships.

BACKGROUNDMajor Depressive Disorder (MDD) is characterized by high neurobiological heterogeneity, which hinders precise diagnosis and treatment. Traditional group-level neuroimaging analyses fail to capture individual differences, while normative modeling offer a promising approach to quantify individual deviations from healthy brain structure patterns, facilitating the identification of biological subtypes and offering a data-driven framework to dissect this heterogeneity. METHODSUsing 1,190 healthy controls, we constructed normative developmental trajectories of gray matter volume (GMV) across 246 Brainnetome-defined regions using Bayesian linear regression. Deviation maps were derived for 398 MDD patients. k-means clustering was employed to identify GMV-based biotypes. Then, the clinical characteristics and anatomical differences among these subtypes were explored, along with the post-treatment clinical features and treatment responses of participants who completed the 8-week antidepressant treatment within each subtype. RESULTSPatients with MDD exhibited widespread yet individually variable GMV deviations. Clustering analysis revealed two subtypes: Subtype 1 displayed predominantly negative deviations in sensorimotor and occipital cortices, whereas Subtype 2 showed widespread positive deviations in temporal and posterior cingulate regions. Subtype 1 had higher extraversion and symptom-linked deviation patterns; in Subtype 2, deviation burden correlated with generalized anxiety. Longitudinally, Subtype 1s GMV deviation changes predicted symptom improvement, while Subtype 2s deviations correlated with baseline severity. CONCLUSIONSNormative modeling of GMV reveals marked neuroanatomical heterogeneity in MDD and identifies subtypes with distinct clinical and treatment-related characteristics, laying a foundation for precision psychiatry and individualized interventions.

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with altered resting-state brain function. An increased excitation-inhibition (E/I) ratio is discussed as a potential pathomechanism but in-vivo evidence of disturbed neurotransmission underlying these functional alterations remains scarce. We compared rs-fMRI local activity (LCOR) between ASD (N=405, N=395) and neurotypical controls (N=473, N=474) in two independent cohorts (ABIDE1 and ABIDE2). We then tested how these LCOR alterations co-localize with specific neurotransmitter systems derived from nuclear imaging and compared them with E/I changes induced by GABAergic (midazolam) and glutamatergic medication (ketamine). Across both cohorts, ASD subjects consistently exhibited reduced LCOR, particularly in higher-order default mode network nodes, alongside increases in bilateral temporal regions, the cerebellum, and brainstem. These LCOR alterations negatively co-localized with dopaminergic (D1, D2, DAT), glutamatergic (NMDA, mGluR5), GABAergic (GABAa) and cholinergic neurotransmission (VAChT). The NMDA-antagonist ketamine, but not GABAa-potentiator midazolam, induced LCOR changes which co-localize with D1, NMDA and GABAa receptors, thereby resembling alterations observed in ASD. We find consistent local activity alterations in ASD to be spatially associated with several major neurotransmitter systems. NMDA-antagonist ketamine induced neurochemical changes similar to ASD-related alterations, supporting the notion that pharmacological modulation of the E/I balance in healthy individuals can induce ASD-like functional brain changes. These findings provide novel insights into neurophysiological mechanisms underlying ASD. One Sentence SummaryLocal activity alterations in ASD co-localize with glutamatergic and GABAergic neurotransmission and were similar to ketamine-induced brain changes.

BackgroundNaturalistic fMRI provides an ecologically valid window into social brain function, yet binary diagnostic labels may obscure neural signatures linked to the continuous spectrum of social deficits. We investigated whether social brain alterations in autism spectrum disorder (ASD) follow a categorical, dimensional, or "dual-track" architecture. MethodsWe analyzed fMRI data from 428 youth (262 ASD, 166 typically developing; ages 5-22) watching two films: The Present and Despicable Me. Using Principal Component Analysis (PCA) to quantify primary (PC1) and secondary (PC2) synchronization, we employed variance partitioning to disentangle the contributions of categorical diagnosis from continuous symptom severity (Social Responsiveness Scale-2, SRS-2). ResultsDuring The Present, reduced synchronization was widespread. In social-motivational hubs (medial prefrontal cortex, caudate), reductions were largely explained by variance shared between diagnosis and SRS-2 scores. In contrast, the left amygdala exhibited a unique dimensional association with SRS-2 scores independent of categorical diagnosis. Secondary response patterns (PC2), reflecting complex temporal integration, revealed further unique dimensional effects in the cuneus. Notably, these signatures were stimulus-dependent, manifesting during the emotionally complex narrative of The Present but not during the slapstick-oriented Despicable Me. ConclusionsWhile core social-motivational hubs reflect overlapping diagnostic and dimensional deficits, the amygdala and secondary visual patterns provide distinct, dimension-specific signatures of social impairment. This variance partitioning approach supports a Research Domain Criteria (RDoC) framework, highlighting the necessity of integrating dimensional assessments and narrative complexity to characterize the neural architecture of autism.

BackgroundLow-grade inflammation occurs in [~]30% of individuals with major depressive disorder (MDD) and is linked to poorer treatment outcomes, autonomic dysregulation, and elevated cardiometabolic risk. Such inflammation may contribute to variability in response to intermittent theta burst stimulation (iTBS) of the left dorsolateral prefrontal cortex. We tested whether baseline inflammation moderates the association between heart-brain coupling (HBC) during iTBS and clinical improvement, and examined neuroimaging correlates of inflammatory status. MethodsInflammation was indexed using routine clinical markers: white blood cell count (WBC) and C-reactive protein (CRP). HBC, a physiological marker of frontal-vagal engagement, was derived from electrocardiographic recordings obtained during the first iTBS sessions. Diffusion MRI free-water metrics were used to assess white-matter microstructural alterations associated with inflammation. ResultsHigher HBC was associated with symptom improvement only among individuals with lower WBC. Patients with higher WBC counts showed elevated free-water diffusion MRI signal in the fornix and corpus callosum, consistent with a neuroimmune profile associated with reduced clinical benefit. ConclusionsBaseline inflammation shapes the clinical relevance of HBC and may help explain inter-individual variability in iTBS efficacy. Routine inflammatory markers could support stratification approaches for biomarker-guided neuromodulation in MDD. HIGHLIGHTSO_LIBaseline WBC moderates HBC-iTBS response in major depression C_LIO_LIHigh HBC predicts improvement only when WBC is low ({approx}<6.44x10/L) C_LIO_LICRP does not significantly moderate HBC-response associations C_LIO_LIHigher WBC links to elevated DTI free-water in the fornix & corpus callosum C_LIO_LIWBC may be a low-cost stratifier for biomarker-guided neuromodulation C_LI

BackgroundEvidence suggests a non-specific mapping between psychiatric disorders and underlying neurobiological substrates. A dimensional psychopathology framework may prove useful for organizing observed neurobiological alterations along broad psychopathological dimensions. MethodsWe applied latent class analysis, with an additional constraint on classification uncertainty, to identify clinical cohorts of symptomatic homogeneity to represent the high-risk end of specific psychopathological dimensions (i.e., internalizing/externalizing, p-factor), using baseline data (N = 11860) from the Adolescent Brain and Cognitive Development (ABCD) Study. These cohorts were compared against neurotypical individuals in deviations from the normality of cortical development, quantified using autoencoder-based normative models, to reveal cortical abnormalities. ResultsWe identified cortical thickness related to psychopathologies in the ABCD data, particularly to externalizing syndromes, and revealed distinct structural abnormalities to broad psychopathological dimensions. ConclusionThis study highlights the value of person-centered analytic techniques, combined with normative modeling, to complement traditional associational methodologies in revealing neurobiological correlates of dimensional psychopathologies.

ImportanceBrain maturation varies between individuals, particularly during dynamic developmental periods like adolescence. Directly assessing differences in longitudinal trajectories can reveal deviations from normative patterns. ObjectiveWe present novel conditional-longitudinal normative models that characterize variability in brain maturation. We utilize these models to examine whether differences in longitudinal trajectories are associated with birth weight (BW), gestational age (GA), and longitudinal psychopathology derived from behavioral assessments. DesignCross-sectional and conditional-longitudinal normative models were developed for brain volumes derived from the first two neuroimaging timepoints from the Adolescent Brain Cognitive Development (ABCD) Study. Conditional-longitudinal models index an individuals expected brain volume at follow-up conditioned on their baseline measurement. Models were fit with split-half cross-validation on demographically matched samples. SettingThe ABCD Study is a multi-site, population-based study ParticipantsParticipants were excluded based on imaging quality flags and missing data, leaving 10,830 at baseline and 7,262 at follow-up. ExposuresBW and GA were derived from parent-report questionnaires. General psychopathology scores were calculated using a bifactor model. Main Outcomes and MeasuresWe calculated cross-sectional and conditional-longitudinal centiles, respectively quantifying individual deviations in size and change between timepoints. Sensitivity analyses included covariates for parental income and education as well as current weight and height. ResultsThe sample was 10,830 at baseline (48.2% F,age 9-10y) and 7,262 at follow-up (46.6% F,age 11-13y). Conditional-longitudinal centiles were sensitive to individual differences in brain change between timepoints. Lower BW was associated with lower conditional-longitudinal centiles, suggesting larger decreases in brain volumes over time (27 regions pfdr<0.05, {beta}max=0.08). Lower conditional-longitudinal centiles were associated with greater increases in psychopathology scores, suggesting with increased psychopathology brain volumes show greater decrease (37 regions pfdr<0.05, {beta}max=0.06). Notably, changes in psychopathology were not related to brain size at either timepoint, indexed by cross-sectional centiles. Conclusions and RelevanceModels that capture individual-level deviations from expected growth trajectories, rather than static positions on a growth curve, are particularly informative for assessing developmental change. Novel conditional-longitudinal models address this gap in lifespan brain imaging. Using this framework, we demonstrate robust associations between individual trajectory deviations, perinatal adversity, and longitudinally assessed mental health symptoms. Condition-longitudinal models hold promise for applications across psychiatric neuroscience, from development to aging. Key Points QuestionHow do differences in brain maturation trajectories, quantified by novel conditional-longitudinal models, relate to perinatal factors and mental health in adolescence? FindingsIn this longitudinal analysis of neuroimaging data from the Adolescent Brain Cognitive Development (ABCD) Study, conditional-longitudinal normative models revealed that trajectories of brain maturation in adolescence are associated with birth weight, and with longitudinal changes in mental health. MeaningConditional longitudinal models detect inter-individual variability in brain maturation, which is related to both perinatal factors and concurrent changes in psychopathology.

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

The flexible deployment of cognitive control is essential for adaptive functioning in dynamic environments given limited cognitive resources. That flexibility depends on rapid detection and resolution of control- prediction errors (CPEs) when current demands diverge from the control plan. Deficits in control and control flexibility are common in psychiatric disorders, yet targeted interventions are limited by incomplete circuit- level understanding and limited means for modulating control circuits . We analyzed two intracranial electroencephalography datasets (one with brief internal capsule stimulation, ICS) to identify a human neurocomputational mechanism for CPE resolution and to test its modifiability. A third dataset of patients receiving internal capsule deep brain stimulation (IC DBS) assessed clinical relevance of modifying CPE-related processes. Phase-amplitude coupling (PAC) anchored to the {theta} phase of right rostral anterior cingulate cortex (rACC-R), especially {theta}-{gamma} coupling between rACC-R and nodes of the cognitive control network (dorsolateral prefrontal cortex, dlPFC; dorsal ACC, dACC), was associated with faster CPE resolution. An adaptive drift-diffusion model indicated that ICS improves control flexibility specifically under high CPE, and mediation analyses showed that this behavioral improvement is mediated by CPE-dependent increases in rACC-R {theta}-centered PAC. In a psychiatric cohort (N=14; primarily treatment-resistant depression, TRD) with IC DBS, enhanced control flexibility, rather than CPE-independent general cognitive control, was strongly associated with clinical response (AUC = 0.90), suggesting both a behavioral flexibility index and rACC-R PAC as candidate biomarkers for DBS optimization. These findings identify a rACC-centered, {theta} phase-based coordination of the cognitive control network as a neurocomputational substrate of flexible control. They demonstrate that capsule stimulation selectively augments this substrate when flexibility is required, and establish flexibility, rather than general control, as the feature that tracks therapeutic benefit in TRD. Together, they suggest actionable biomarkers to guide, personalize, and potentially enable closed-loop neuromodulation for disorders marked by cognitive rigidity.

ImportanceMajor depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. ObjectiveTo use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design, setting, and participantsA normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measuresZ-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. ResultsZ-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevanceOur study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points QuestionCan z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? FindingWe confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. MeaningOur findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

Exposure is considered the most active element of cognitive behavioral therapy (CBT) for pediatric anxiety, and its efficacy is theorized to depend on cognitive control and its supporting neural substrates (e.g., central executive [CEN], salience [SN], and default mode networks [DMN]). However, little work has identified how CBT, or exposure specifically, modulates intrinsic connectivity of these networks. Progress may be limited by heterogeneity in network connectivity in anxiety, which may obscure treatment-related effects in group-averaged analyses. This randomized clinical trial (RCT) leverages person-specific network modeling to test how exposure-focused CBT (EF-CBT) influences resting-state connectivity of cognitive control networks in pediatric anxiety, relative to an active control (relaxation mentorship training; RMT). Youth aged 7-18 years with an anxiety disorder (N = 104) or low/no anxiety (L/NA; N = 37) completed resting-state fMRI scans before being randomized to EF-CBT or RMT. Resting-state connectivity was reassessed following treatment (or commensurate time L/NA youth) in 113 participants. Changes in within-CEN, CEN-SN, and CEN-DMN density were examined using Group Iterative Multiple Model Estimation, which yields sparse, person-specific networks capturing both shared and individual connectivity structure. At baseline, anxious youth exhibited lower density within-CEN, between CEN-SN, and between CEN-DMN than L/NA youth. Treatment effects differed by intervention: EF-CBT selectively increased (i.e., normalized) CEN-SN density, whereas RMT increased within-CEN density. These findings demonstrate dissociable effects of exposure and relaxation on cognitive control network organization in pediatric anxiety. Exposure-focused CBT uniquely strengthens coordination between control and salience systems, consistent with a mechanism supporting top-down control of threat-related signals during exposure. Network-based measures of cognitive control may help identify mechanistic targets for optimizing and personalizing treatment. Clinical Trial NumberNCT02810171.

Intermittent theta burst stimulation (iTBS) is a well-established treatment for major depressive disorder (MDD), but predicting clinical outcomes remains challenging. Heart rate modulation induced by iTBS has emerged as a potential biomarker for treatment response, yet the role of white matter (WM) properties in mediating these effects is largely unexplored. In this quadruple-blind, crossover study, we investigated the relationship between WM microstructure, iTBS-driven heart rate deceleration, and antidepressant effects. Using correlational tractography, we analyzed four major WM tracts--the cingulum, fornix, superior longitudinal fasciculus, and uncinate fasciculus--and examined short-term WM microstructural changes to assess their predictive value for therapeutic outcomes. Baseline WM findings revealed that in the fornix and right dorsal cingulum fractional anisotropy (FA) negatively correlated with heart rate deceleration. Radial and mean diffusivity (MD, RD) in the fornix positively correlated with heart rate deceleration. FA in the right ventral cingulum positively correlated while MD and RD negatively correlated with symptom improvement. Longitudinally, FA increases in the left cingulum were significantly associated with greater symptom alleviation after treatment. Notably, iTBS-induced heart rate modulations correlated with clinical improvement after six weeks, while WM microstructural properties in the fornix and cingulum demonstrated predictive value for both heart rate modulation and treatment response. WM changes in the cingulum, evident as early as four weeks, highlight its unique neuroplasticity potential along iTBS intervention. Together, these findings provide novel insights into the structural connectivity patterns influencing iTBS outcomes, offering a novel foundation for more personalized therapeutic strategies in MDD.

BACKGROUNDConvergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. However, brain functional connectivity patterns that underpin the P factor remain poorly understood, especially at the transition to adolescence when many serious mental disorders have their onset. OBJECTIVEIdentify a distributed connectome-wide neurosignature of the P factor and assess the generalizability of this neurosignature in held out samples. DESIGN, SETTING, AND PARTICIPANTSThis study used data from the full baseline wave of the Adolescent Brain and Cognitive Development (ABCD) national consortium study, a prospective, population-based study of 11,875 9- and 10-year olds. Data for this study were collected from September 1, 2016 to November 15, 2018 at 21 research sites across the United States. MAIN OUTCOMES AND MEASURESWe produced whole brain functional connectomes for 5,880 youth with high quality resting state scans. We then constructed a low rank basis set of 250 components that captures interindividual connectomic differences. Multi-level regression modeling was used to link these components to the P factor, and leave-one-site-out cross-validation was used to assess generalizability of P factor neurosignatures to held out subjects across 19 ABCD sites. RESULTSThe set of 250 connectomic components was highly statistically significantly related to the P factor, over and above nuisance covariates alone (ANOVA nested model comparison, incremental R-squared 6.05%, {chi}2(250) =412.1, p<4.6x10-10). In addition, two individual connectomic components were statistically significantly related to the P factor after Bonferroni correction for multiple comparisons (t(5511)= 4.8, p<1.4x10-06; t(5121)= 3.9, p<9.7x10-05). Functional connections linking control networks and default mode network were prominent in the P factor neurosignature. In leave-one-site-out cross-validation, the P factor neurosignature generalized to held out subjects (average correlation between actual and predicted P factor scores across 19 held out sites=0.13; pPERMUTATION<0.0001). Additionally, results remained significant after a number of robustness checks. CONCLUSIONS AND RELEVANCEThe general factor of psychopathology is associated with connectomic alterations involving control networks and default mode network. Brain imaging combined with network neuroscience can identify distributed and generalizable signatures of transdiagnostic risk for psychopathology during emerging adolescence.