Brain regions specialized for language have been extensively described, yet their arrangement into one or multiple networks remains debated. Using precision functional mapping across three independent cohorts of intensively scanned individuals (22 individuals scanned over 216 separate MRI sessions), we dissociated two nested left-lateralized perisylvian networks: an intermediate language network (intLANG) and an anatomically distinct association language network (aLANG). intLANG is anchored to precentral speech areas and the Sylvian parietal-temporal area (Spt), whereas aLANG surrounds intLANG and extends into higher-order prefrontal and temporal association cortices. The two networks can be fully recapitulated by functional connectivity from adjacent cerebellar regions, indicating that they are segregated, brain-wide networks. Task-based analyses further reveal that intLANG and aLANG are functionally distinct: intLANG responds robustly during rhyme judgments and nonword reading that emphasize phonology, whereas aLANG is preferentially recruited during meaning-based sentence processing. These findings indicate that human language engages nested distributed networks each specialized for distinct components of language processing: a lower-order network biased toward phonology, and a surrounding association network that subserves higher-order syntax and semantics. This nested organization is similar to other brain systems suggesting a shared hierarchical motif that may give rise to specialized cognitive functions across the human brain.

BACKGROUND:Major depressive disorder remains a leading cause of disability, and approximately one-third of patients do not respond to standard treatments and develop treatment-resistant disease. Progress toward personalized neuromodulation has been limited by the lack of objective, brain-based biomarkers that can guide circuit target selection and track symptom burden in real time. Here we test whether the aperiodic exponent of intracranial EEG local field potentials provides a neurophysiological marker of current depressive symptom severity. METHODS:We analyzed resting-state intracranial EEG from a cohort of patients undergoing invasive monitoring for refractory epilepsy (N = 20), comprising >1,800 intracranial contacts sampling distributed cortical and subcortical regions, with depressive symptoms quantified immediately prior to recording using the Beck Depression Inventory-II. RESULTS:Region- and network-level analyses localized aperiodic exponent effects to frontolimbic and insular circuits-including orbitofrontal cortex, anterior cingulate cortex, insula, and amygdala-and to large-scale salience and default-mode networks, where exponents scaled continuously with symptom burden. In addition, exponents within the salience network tracked anhedonia. The whole-brain mean aperiodic exponent discriminated minimal versus elevated depressive symptom status (area under the curve = 0.82). CONCLUSIONS:Together, these results identify intracranial aperiodic exponents as a scalable, circuit-relevant marker of current depressive symptom burden, with potential utility for biomarker-informed, individualized neuromodulation in major depressive disorder.

Roughly one-third of patients with major depressive disorder (MDD) fail to respond to standard treatments and develop treatment-resistant MDD. For these patients, alternative therapies ofer additional options but yield inconsistent outcomes. Progress has been limited by the absence of objective, brain-based biomarkers to guide target selection or track therapeutic response in real time. Instead, clinicians rely on behavioral assessments that evolve slowly over weeks to months, obscuring the underlying neural dynamics of symptom changes. Here, we test whether the aperiodic exponent of intracranial EEG (iEEG) local field potentials can serve as a neurophysiological marker of depression symptom severity. We leveraged a large iEEG cohort (N = 20) undergoing invasive monitoring for refractory epilepsy, yielding over 1,800 contacts spanning cortical and subcortical zones. For each contact, we estimated the aperiodic exponent (thought to reflect aspects of cortical excitability) of the power spectrum across 10-100 Hz within local brain regions and across distributed cortical association networks. Depressive symptoms were assessed with the Beck Depression Inventory-II (BDI-II) immediately before intracranial resting state recordings. With respect to the BDI-II scale, participants were identified as experiencing minimal (BDI-II ≤ 13) or elevated depression symptoms (BDI-II ≥ 14). Associations between symptom severity (BDI-II total score and Somatic-Afective, Cognitive, and Anhedonia subscales) and region- or network-level exponents were modeled with ordinary least squares (OLS) regression. The whole-brain, mean aperiodic exponent for each participant discriminated symptom status (AUC = 0.81). At the regional level, the orbitofrontal cortex, anterior cingulate cortex, insula, and amygdala showed higher exponents in the elevated depression symptom group (d = 1.18-1.71; p = 0.032-0.004). A post-hoc classification analysis across these four regions misclassified one participant per group (AUC = 0.86; 95% CI 0.64-1.00). In continuous analyses, BDI-II scores correlated positively with exponents in these same four regions (pFDR = 0.019-0.027; partial r=0.61–0.70) and at the network level in the Salience network (pFDR = 0.024; partial r = 0.63) and Default (pFDR = 0.046; partial r = 0.55) network. The Salience network significantly tracked Anhedonia symptoms (p = 0.004; partial r = 0.62). Here we report that intracranial aperiodic exponents within fronto-limbic and insular circuits, overlapping with networks implicated in contemporary accounts of depression pathophysiology, diferentiate depressive symptom status and scale with severity. These findings support the aperiodic exponent as a candidate neurophysiological marker of current depression symptom burden, with potential relevance for individualized neuromodulation in MDD.

Human prefrontal cortex (PFC) is heterogenous. In monkeys, side-by-side PFC regions, including within dorsolateral PFC (DLPFC), show distinct long-range anatomical projection profiles, raising the possibility that adjacent regions might specialize as a consequence of the distributed networks in which they are embedded. Consistent with this possibility, recent findings in humans provide evidence for PFC regions domain-specialized for scene (spatial) processing and language processing that are adjacent to distinct domain-flexible regions responding to traditional cognitive control demands. Here we tested functional specialization of PFC regions linked to another domain-specialized network recruited by certain forms of social processing (theory-of-mind, ToM, tasks). Using within-individual precision neuroimaging approaches, side-by-side DLPFC regions embedded within parallel distributed networks were identified within the idiosyncratic anatomy of each individual (N=13). Functional responses between these distinct regions revealed a robust functional double dissociation: one DLPFC region was preferentially recruited by ToM tasks and an adjacent DLPFC region by working memory task demands. The region responding to social processing demands was small and its position varied slightly from one person to the next suggesting why it may have been underappreciated in past group-based analyses. These findings add to evidence that PFC features more functional specialization than commonly appreciated and further that the specialization of juxtaposed regions within PFC can be understood by examining the distributed networks within which the regions are embedded.

Longitudinal studies are required to measure individual differences in human brain aging, but are challenging over short intervals due to measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assess brain aging in individuals across three timepoints in one year. Cluster scanning substantially improves the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, we detect expected differences in the rates of brain aging between younger and older individuals, as well as differences between cognitively unimpaired and impaired individuals. Cognitively unimpaired older individuals variably reveal relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. We observe these atypical brain aging trajectories across structures and verify them in independent within-individual test-retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change.

Neuroanatomical findings on panic disorder (PD) are typically difficult to replicate, with inconsistent effects. These concerns prompted a paradigm shift towards large-scale collaborations, focused on harmonized data extraction and processing for robust examination of PD brain correlates. Hence, leveraging the largest-ever multi-site neuroimaging database on PD (Age: 10–66 years; global sites: 28), compiled by the ENIGMA-Anxiety Working Group, we report on cortical and subcortical differences in individuals with PD (N = 1146) versus healthy controls (HC: N = 3778). The analyses revealed lower thickness and smaller cortical surface area within fronto-temporo-parietal regions in PD (Cohen’s ds: −0.08–0.13), along with lower thalamic and caudate volumes (Cohen’s ds: −0.07–0.12). Diagnosis-by-age2 interactions (Cohen’s ds: 0.07–0.12) revealed lower thickness in individuals with PD compared to HC in certain regions during adulthood (25–55 years), with relative absence of such differences during youth (<25 years) or late adulthood (>55 years). Finally, patient subgroup analyses showed that early disease onset (≤21 years) in PD was associated with larger lateral ventricles (Cohen’s ds: 0.31–0.38), whilst no medication, comorbidity, or severity effects were found. These findings lend support to neurocircuitry models of PD, which postulate differences within fronto-striato-limbic circuits and temporo-parietal regions. Moreover, findings highlight the potential importance of abnormal development and aging in neuroanatomical differences related to PD. Given its unprecedented scale, the current study is an important milestone towards identifying the structural brain correlates of PD.

Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer's disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for detecting longitudinal atrophy over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual participants. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2'23"). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over 2 days. Using linear mixed-effects models, we found that phenotypically vulnerable cortical ("core atrophy") regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

Purpose:Compressed-sensing (CS) methods can decrease the acquisition time for T1-weighted (T1w) structural MRI images to 1-2 min. Rapid acquisitions reduce participant burden, reduce the risk of motion artifacts, and allow for repeat scans to be acquired within a session. This study investigated the tradeoffs of sparse sampling and CS image reconstruction for brain morphometric applications. Methods:Magnetization-Prepared Rapid Gradient Echo (MPRAGE) images were acquired at 1.0 mm spatial resolution. The effects of the acceleration factor (x2 to x8) and regularization factor were examined. Subcortical volumes and regional cortical thickness estimates of brain structure were obtained for all T1w images. Within-sequence agreement was evaluated by comparing estimates obtained using the same protocol in the same imaging session. Between-sequence agreement was evaluated by comparing estimates from a fully sampled MPRAGE protocol to the novel CS-accelerated MPRAGE protocols within the same session. Results:Higher acceleration lowered the SNR in white matter but not in gray matter. SNR could be further manipulated by the regularization parameter. Within-sequence agreement was comparable across all protocols. In fact, the spread in estimates from the 58-s CSx8 protocol was similar to those from the fully sampled protocol. Similarly, high agreement was found between estimates from the fully sampled and under-sampled protocols for all acceleration levels up to eight. Modifying the regularization factor had a quantifiable effect on image smoothness, however it had minimal impact on the agreement of morphometric estimates. Conclusion:Accelerated CS imaging protocols show comparable performance to traditional longer protocols for morphometric brain estimates.

Prefrontal regions are hypothesized to be organized hierarchically in support of cognitive control. Using precision functional MRI in three independent cohorts of intensively scanned participants (N=37), we consistently identified a lateral prefrontal cortex (LPFC) region linked to a canonical control network separate from a nearby rostral LPFC region linked to the action-mode network. Despite their spatial juxtaposition, the two regions were differentially coupled to the caudate and ventral putamen, suggesting they are components of segregated networks. Working memory demands activated the canonical LPFC control region but not the adjacent region. Contrasting go and no-go trials during target detection revealed a robust functional double dissociation during goal-directed behavior. The canonical LPFC control region activated when responses were withheld, while the putamen-coupled LPFC region increased activity during executed responses. These findings demonstrate that adjacent LPFC regions participate in opposing functions predicted by their embedding within distinct parallel large-scale networks.

BACKGROUND AND HYPOTHESIS:Schizophrenia spectrum disorders (SSDs) produce severe symptoms, disability, and premature mortality, but only partially effective symptomatic treatments exist. Treatment development is impeded by lack of insight into disease mechanisms or objective biomarkers for clinical trials. Advances in genetics and neurobiology have converged on strong pathogenic hypotheses for SSDs centered on synapse dysfunction and excessive pruning, pathogenic processes that may produce measurable proteomic evidence in cerebrospinal fluid (CSF). Leveraging design precedents from successful fluid biomarkers discovery for Alzheimer's disease, we undertook a pilot study to test the feasibility of repeated CSF and blood samples collection from individuals with SSDs. Here we report on successful implementation of longitudinal bio-behavioral phenotyping in SSDs and establishment of a repository to permit broad sample and data sharing. STUDY DESIGN:The Schizophrenia Spectrum Biomarkers Consortium (SSBC) study principles included longitudinal study design, paired CSF and plasma collection associated with robust phenotypic characterization, at 3 academic sites and the establishment of a biorepository. Participants underwent clinical and cognitive assessments, neuroimaging, blood draws, and CSF collection via lumbar puncture (LP) every 6 months. STUDY RESULTS:SSBC successfully enrolled 48 SSD and 41 Healthy Controls with a 73% longitudinal retention. Clinical, cognitive, and neuroimaging results were consistent across sites and with existing studies. Study procedures were well tolerated, and almost all LPs (99%) resulted in either no or minor headache/backache that resolved without medical interventions. CONCLUSIONS:The pilot SSBC study demonstrates that a multi-site, longitudinal study with repeat CSF collection is feasible, with excellent participant acceptability and retention.

Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and modeling of the TMS electric field (E-field), we asked whether various clinical TMS approaches target different functional networks between individuals. Results revealed that modeled homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. TMS coil positions over the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC most frequently target a network coupled to the ventral striatum (reward circuitry) but largely miss that network in some individuals. We further illustrate how modeling can be used to retrospectively assess the estimated targets achieved in prior TMS sessions and also used to prospectively provide coil positions that can target distinct closely localized dlPFC network regions with spatial selectivity and maximal E-field intensity. In a final study, precision targeting was found to be feasible in participants with Major Depressive Disorder using data derived from a single low-burden MRI session suggesting the methods are applicable to translational efforts where limiting patient burden and ensuring robustness are critical.

The ventral striatum (VS) receives input from the cerebral cortex and is modulated by midbrain dopaminergic projections in support of processing reward and motivation. Here, we explored the organization of cortical regions linked to the human VS using within-individual functional connectivity MRI (fcMRI) in intensively scanned participants. In two initial participants (scanned 31 sessions each), seed regions in the VS were preferentially correlated with distributed cortical regions that are part of the salience network. The VS seed regions recapitulated salience network topography and replicated in each individual, including anterior and posterior midline regions, anterior insula, and dorsolateral prefrontal cortex (DLPFC). The topography was distinct from adjacent striatal seed regions and from cortical networks associated with domain-flexible cognitive control. Unbiased comprehensive analyses of the full striatum confirmed that the VS is coupled to the salience network while also revealing the established, spatially separated cognitive zones of the caudate and motor zones of the putamen. VS correlation with the salience network, including DLPFC, was observed in 15 additional participants (scanned 8 or more times each), indicating it is a robust and generalizable finding. These results suggest that the VS contributes to a cortico-basal ganglia loop that is part of the salience network and raise the possibility that the DLPFC may be an effective neuromodulatory target for neuropsychiatric disorders of reward and motivation because of its preferential coupling to the VS.NEW & NOTEWORTHY Individualized precision neuroimaging reveals the ventral striatum (VS) is preferentially correlated with the salience network, including a region in the dorsolateral prefrontal cortex (DLPFC) that is adjacent to regions associated with cognitive control. These results raise the possibility that DLPFC is an effective neuromodulatory target for depression due to preferential coupling with the VS.

Precision mapping of brain networks within individuals prevailingly relies on functional connectivity analysis of resting-state data. Here, we explored whether networks can be estimated using only task data. Correlation matrices estimated from task data were similar to those derived from resting-state data. The largest factor affecting similarity was the amount of data. Precision networks estimated from task data showed strong spatial overlap with those derived from resting-state data and predicted the same triple functional dissociation in independent data. To illustrate novel possibilities enabled by the present methods, we mapped the detailed organization of thalamic association zones within individuals by pooling extensive resting-state and task data. We also demonstrated how task data can be used to estimate networks while simultaneously extracting task responses. Broadly, these findings suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

The hippocampus possesses anatomical differences along its long axis. Here, we explored the functional specialization of the human hippocampal long axis using network-anchored precision functional MRI in two independent datasets (N = 11 and N = 9) paired with behavioral analysis (N = 266 and N = 238). Functional connectivity analyses demonstrated that the anterior hippocampus was preferentially correlated with a cerebral network associated with remembering, while the posterior hippocampus selectively contained a region correlated with a distinct network associated with behavioral salience. Seed regions placed within the hippocampus recapitulated the distinct cerebral networks. Functional characterization of the anterior and posterior hippocampal regions using task data identified and replicated a functional double dissociation. The anterior hippocampal region was sensitive to remembering and imagining the future, specifically tracking the process of scene construction, while the posterior hippocampal region displayed transient responses to targets in an oddball detection task and to transitions between task blocks. These findings suggest an unexpected specialization along the long axis of the human hippocampus with differential responses reflecting the functional properties of the partner cerebral networks.

Sleep is a fundamental biological process associated with diverse physiological and psychological functions, yet systematic, population-level, objective descriptions of its variation across demographic and psychological factors are still emerging. Here, we characterize age- and sex-related differences in sleep and their associations with mood using week-long actigraphy data from UK Biobank participants aged 44-82. Robust age- and sex-related differences in sleep were identified (n = 38 546) and replicated (n = 38 547), reflecting reliable nonlinear interactions between age and sex. Younger women slept about 17 min more than their male counterparts, though this difference diminished with age, with both sexes reducing total sleep duration in later life. Middle-aged individuals exhibited shorter sleep durations during the week, with weekend sleep increasing by as much as 50 min. Participants in their seventh and eighth decades showed more consistent sleep patterns throughout the week. Sleep patterns also suggest maintenance of total sleep duration: individuals reporting waking too early maintain sleep duration by going to sleep earlier, while individuals reporting sleeping too much fall asleep later but also wake later, again maintaining sleep duration. Self-reported depression and anhedonia were associated with reduced total sleep duration across multiple age groups and both sexes. By systematically mapping actigraphy-derived sleep features across demographic strata and linking them to subjective reports of sleep and mood, this study provides an integrated framework that complements and extends prior findings, offering a valuable reference point for future investigations of sleep-mood associations in large cohorts.

Longitudinal studies are required to measure individual differences in human brain aging, but they are difficult to estimate over short intervals because of measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assessed brain aging in individuals across three longitudinal timepoints spaced across one year. Cluster scanning substantially improved the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, expected differences in the rates of brain aging between younger and older individuals were evident, as were differences between cognitively unimpaired and impaired individuals. Each person's brain change trajectory was compared to modeled normative expectations from a large cohort of age-matched UK Biobank participants. Cognitively unimpaired older individuals variably revealed relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. These atypical brain aging trajectories were found across structures and verified in independent within-individual test and retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change.

Robust age- and sex-related differences in sleep were identified (n=38,546) and replicated (n=38,547) from week-long passive actigraphy data in UK Biobank participants ages 44-82. Sleep patterns reflected reliable non-linear interactions between age and sex. Younger women slept about 17 min more than their male counterparts, though this difference diminished with age, with both sexes reducing total sleep duration in later life. Middle-aged individuals exhibited shorter sleep durations during the week, with weekend sleep increasing by as much as 50 min. Participants in their seventh and eighth decades showed more consistent sleep patterns throughout the week. Sleep patterns also suggest maintenance of total sleep duration: individuals reporting waking too early maintain sleep duration by going to sleep earlier, while individuals reporting sleeping too much fall asleep later but also wake later, again maintaining sleep duration. Self-reported depression and anhedonia were associated with reduced total sleep duration across multiple age groups and both sexes. These collective results indicate that the timing, consistency, and overall amount of sleep differs by age and is affected by individual factors.

Experiencing stressful life events has been associated with poor mental health outcomes, but less is known about daily stressful events’ more proximate impacts on daily behavior and affect. Here we leverage mobile and wearable technology and recent advances in natural language processing for a fine-grained examination of first-year college students’ daily life stress, with a focus on academic and social domains, over a full academic year (8,000+ total daily observations). Experiences of stress were characterized from participants’ daily voice diaries narrating the main events of the day, using a combination of expert human labeling and large language models fine-tuned for sentiment analysis and topic modeling. Bayesian hierarchical models assessed within-person associations between diary-derived instances of academic and social stressful events and same-day sleep, physical activity, social activity, and negative affect measured with actigraphy wristbands and phone surveys. Days with academic stressful events were associated with shorter sleep duration, decreased physical activity, reduced desire to be around others, and modestly increased negative affect. Additionally, days with academic stressful events had significantly reduced social interaction and increased time spent on schoolwork relative to days with social stressful events. Meanwhile, days with social stressful events stood out by especially heightened negative affect, above and beyond the effect of academic stressful events. Our results suggest that academic and social dimensions of life stress may present distinct signatures in daily affect and behavior, with potential implications for long-term wellbeing.

The striatum receives projections from multiple regions of the cerebral cortex consistent with its role in diverse motor, affective, and cognitive functions. Supporting cognitive functions, the caudate receives projections from cortical association regions. Building on recent insights about the details of how multiple cortical networks are specialized for distinct aspects of higher-order cognition, we revisited caudate organization using within-individual precision neuroimaging (n=2, each participant scanned 31 times). Detailed analysis revealed that the caudate has side-by-side zones that are coupled to at least Give distinct distributed association networks, paralleling the specialization observed in the cerebral cortex. Examining correlation maps from closely juxtaposed seed regions in the caudate recapitulated the Give distinct cerebral networks including their multiple spatially distributed regions. These results extend the general notion of parallel specialized basal ganglia circuits, with the additional discovery that even within the caudate, there is Gine-grained separation of multiple distinct higher-order networks.