NeuroImage: Reports

Abstract Background The neuropsychiatric sequelae (cognitive deficits, anxiety and depression) post-COVID-19 exist as part of the spectrum of the multisystemic long COVID. People with these symptoms have been shown to have worse functional outcomes. Hence, this significantly impacts the economy, primarily due to productivity losses. Yet, the precise underlying pathogenesis remains unknown, as do any diagnostic or prognostic biomarkers. Aim This study aims to identify functionally impacted brain regions in those with post-COVID-19 neurological sequelae and to evaluate how alterations in brain function relate to acute serum inflammatory mediators and brain injury biomarkers. Methods The study will utilise data from the COVID-19 Clinical Neuroscience Study (CNS), a large UK multicentre study conducted during the pandemic. This sub-study will be a secondary analysis of the COVID-19 CNS, a prospective observational study that compared COVID-19-positive participants with negative controls. Participants had an MRI, blood tested for acute serum inflammatory mediators and brain injury biomarkers, and neuropsychiatric assessment (cognitron test, PHQ-9 and GAD-7) post-acutely and at follow-up. The DPABI (Data Processing & Analysis for Brain Imaging) toolbox will be used for pre-processing and analysis of the neuroimaging data (resting state functional MRI) to obtain regional homogeneity (ReHo). The association between ReHO and neuropsychiatric symptoms, acute inflammatory mediators and brain injury biomarkers will be explored. Regression models would be used to determine the risk and protective factors for developing these symptoms. Conclusion It is anticipated that the study will provide new insights into the functional basis of post-acute COVID-19 neuropsychiatric manifestations and identify new diagnostic biomarkers. Key words: Regional Homogeneity, COVID-19, functional MRI, neuropsychiatry, biomarkers

Objective. Intermittent theta burst stimulation (iTBS) is a repetitive transcranial magnetic stimulation (rTMS) protocol used in clinical settings to alleviate symptoms of psychiatric disorders. Although the pulse parameters currently used in iTBS have remained unchanged since the first treatments, recent controllable TMS devices allow for the personalization of pulse shape and duration, opening the possibility of testing the efficacy of other pulse parameters in modulating corticospinal excitability (CSE). Methods. In a repeated-measures design, changes in CSE were induced by the administration of iTBS over the left primary motor cortex under three different conditions: long bidirectional pulse (L-Bi), long quasi-unidirectional pulse (L-QU), and a control short quasi-unidirectional pulse (S-QU). We assessed TMS-induced motor evoked potentials before and up to 70 minutes after iTBS. We hypothesized that L-QU pulses would induce greater facilitation than other conditions. Results. A mixed linear model showed that all conditions induced increased MEPs compared to baseline. However, the L-Bi pulse led to a larger and more sustained MEP increase than both unidirectional pulses, while the S-QU pulse appeared less effective in modulating CSE. We observed large within- and between-subject variability in responsiveness to the iTBS protocols. Conclusion. These results suggest that L-Bi pulses in iTBS protocols are more potent than unidirectional pulses in inducing CSE changes in the cortex of healthy adults. The individual variability in response to iTBS and the potential clinical relevance of these findings are discussed.

Background and Objective: For their acceptance as endpoints in clinical trials, biomarkers and clinical outcome assessment (COA) measures should show reliability by being reproducible (precise) and responsive to change. We evaluated precision and responsiveness of MRI-derived brain volume metrics and various COAs in ceroid-lipofuscinosis, neuronal-3 (CLN3) disease. Methods: This longitudinal observational study (NCT03307304) included 44 individuals with CLN3-related disease (ages 0.5-29.1 years, 48% female). Brain volumetric measures were computed using SynthSeg-v2.0 from T1-weighted 3D MPRAGE images acquired at 3T (Median [Q1, Q3] total MRI follow-up duration from baseline: 2.04 [0.00, 3.69] years). Compartmental tissue volumes were normalized to a model-adjusted total intracranial volume and scaled to the tissue-specific volume fraction expected in healthy adolescent brains to extract measures of brain atrophy. COAs included the Vineland Adaptive Behavior Scales, verbal IQ, and the Unified Batten Disease Rating Scale (UBDRS). Analysis of linear mixed-effects models included only CLN3 participants with syndromic presentation, having three or more visits per measurement to estimate slope, its relative standard error (RSE), and the minimum inter-visit interval (Tmin) for statistically significant progression for each participant. Tmin to detect progression in 75% of the population (Tmin,75%) was computed for each metric. Results: Among brain volumetric measures, brain parenchyma volume (BPV) and gray matter volume (GMV) were the most precise and responsive metrics. BPV showed an overall intra-individual decline with age of -1.32%/year (95% CI: -1.64% to -0.99%), low RSE of 12.5%, and short Tmin,75% of 0.85 years (95% CI: 0.47-1.24). Amongst COAs, the UBDRS Clinical Global Impression (CGI) score was the most precise and responsive with an overall intra-individual increase of 1.26 units/year (95% CI: 1.03-1.49), RSE of 24.10%, and Tmin,75% of 2.56 years (95% CI: 1.73-3.46), though with lower precision (RSE higher) and lower responsiveness (Tmin,75% longer) than BPV. Discussion: MRI-derived metrics, in particular BPV and GMV, detected change in brain atrophy with high precision and responsiveness suggesting that they would be informative for evaluating potential neuroprotective therapies in CLN3 disease. These findings support incorporating MRI-derived volumetric measurements alongside clinical assessments in future clinical trials in CLN3.

BackgroundThe balance between excitatory and inhibitory neuronal signalling (E/I balance), proposed as a key mechanism controlling neuronal activity, is disrupted in many neurodevelopmental conditions. One such condition is Neurofibromatosis Type 1, a monogenic neurodevelopmental disorder characterised by increased inhibitory tone. While previous research suggests that E/I balance may be altered in infancy, it is unknown whether E/I balance remains altered during adolescence in NF1. Here we investigated this question using the aperiodic exponent of the resting electroencephalogram (EEG) power spectrum, a proposed electrophysiological index of E/I balance. MethodThe aperiodic exponent of the EEG power spectrum ( 1/f) was estimated for 16 adolescents with NF1 diagnosis aged 11-16, and 16 age- and sex-matched neurotypical controls. General linear models (GLM) were fit to estimate effects of NF1 diagnosis, age and their interaction on aperiodic exponent. We also estimated the peak dominant frequency and power in oscillatory activity of the EEG signal and fitted the same structure of GLM models. Cognitive assessments across domains of memory, executive function and attention were also analysed with GLM to examine brain-cognition associations. ResultsThe NF1 group had higher aperiodic exponent compared to neurotypical controls, but the aperiodic exponent was not associated with cognitive performance. After accounting for aperiodic activity, the NF1 group also had lower dominant oscillatory frequency than the control group. There was no significant interaction between NF1 diagnosis and age on PAF. There were no group differences in peak alpha power. ConclusionsWe demonstrate for the first time that the alterations to aperiodic activity in NF1 are consistent with the proposed mechanism of increased inhibitory tone. These findings show the potential of this paradigm for future pharmacological and brain stimulation trials investigating treatment-related changes in cortical function.

Background and Objectives Transient ischemic attack (TIA) diagnosis is challenging because it relies mainly on clinical assessment and exclusion of common differential. Functional MRI (fMRI) may provide a diagnostic biomarker by detecting transient brain dysfunction not visible on conventional imaging. This pilot study aimed to characterize acute motor-task BOLD activation in patients with suspected DWI-negative TIA and compare functional patterns between confirmed TIA and alternative diagnoses. Secondary objectives were to assess longitudinal changes in activation and connectivity at 3 months. Methods In this prospective single-center study, adults presenting within 24 h of transient anterior-circulation neurological symptoms underwent conventional MRI and additional motor-task fMRI. Patients with acute DWI lesions were excluded. Final diagnosis was established after 3-month follow-up. Whole-brain analyses, individualized motor cortex region-of-interest (ROI) analyses, and generalized psychophysiological interaction (gPPI) connectivity analyses were performed during the acute phase and at 3 months. Results Eight patients were enrolled; six had complete longitudinal imaging data and were included in the final analyses. Final diagnoses were TIA (n=2), migraine aura (n=2), functional neurological disorder (n=1), and hypoglycemia (n=1). Whole-brain and ROI analyses showed reduced bilateral motor-related BOLD responses during the acute phase in both TIA patients and in the patient with hypoglycemia. In contrast, patients with migraine and functional neurological disorder exhibited less marked longitudinal changes. In TIA patients, motor cortex activation increased at 3 months, tending to normalize toward values observed in non-TIA participants. Connectivity analyses revealed heterogeneous patterns, but the 2 TIA patients demonstrated increased longitudinal connectivity between the motor cortex contralateral to symptoms and other sensorimotor regions, including supplementary motor, premotor, parietal, and postcentral cortices. Discussion These preliminary findings suggest that TIA may be associated with transient bilateral reductions in motor-network activity. Functional abnormalities appeared to improve over time, supporting the hypothesis of reversible network dysfunction. Although limited by the very small sample size, this study provides the first longitudinal assessment of emergency task-based fMRI in rigorously defined TIA and supports further investigation of acute functional MRI as a potential biomarker of transient cerebral dysfunction.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Background: Behavioral variant frontotemporal dementia (bvFTD) is clinically defined by progressive changes in personality, executive function, and language, yet the structural neural correlates of symptom severity remain unclear. This study aimed to assess the association between clinical findings and neuroimaging biomarkers. Methods: We included 87 patients with bvFTD. Clinical functioning was covering behavior and language. Spearman rank correlations were used to assess univariate associations, and Bonferroni correction was applied for multiple comparisons. Principal component analysis (PCA) was conducted on regional volumes, and multivariable logistic regression was used to model high behavioral impairment (FBI total [≥]30), adjusting for age, sex, and education. Results: No individual regional volume was significantly correlated with clinical scores after Bonferroni correction (all adjusted p>0.05), and PCA-derived imaging components failed to predict behavioral severity in multivariable models. Age emerged as the only significant predictor (OR=1.11 per year; p=0.009). Model discrimination was modest (AUC=0.735) and did not improve with the inclusion of imaging features (p=0.453 vs. demographics-only model). Conclusions: In this bvFTD cohort, structural volumes of key frontotemporal and perisylvian regions did not predict behavioral or language impairments after correction for multiple comparisons. These findings show the limitations of regional volumetric analysis in isolation and stress the need for network-based, multimodal, and longitudinal approaches.

Syntax processing impairment is a defining feature of primary progressive aphasia (PPA), particularly in the nonfluent/agrammatic variant. However, current models are derived largely from Indo-European languages, and it remains unclear whether the cognitive and neuroanatomical mechanisms of syntax processing generalize to Chinese, an analytic, pro-drop language that encodes grammatical relationships primarily through word order and function words rather than inflectional morphology. Participants were recruited from the international Chinese Language Assessment in PPA cohort and included 64 individuals with PPA (19 logopenic, 15 nonfluent/agrammatic, 15 semantic, and 15 unclassifiable/mixed) and 60 cognitively healthy controls. Participants completed sentence comprehension and production tasks with systematically manipulated syntactic features. Item-level linguistic determinants were examined using ridge regression, diagnostic performance using receiver operating characteristic analyses, and neuroanatomical correlates using voxel-based morphometry of structural MRI. Performance on sentence comprehension and production was influenced by grammatical and lexical features. Subject-relative clauses, passive constructions, a higher proportion of function words, longer character length, and greater lexical content were associated with poorer performance among participants with PPA. Sentence comprehension was impaired in the logopenic, nonfluent/agrammatic, and semantic variants, even when isolated to the syntactic component of the sentence comprehension task. Similarly, sentence production was impaired across all PPA variants (all P < 0.01 versus controls). No significant differences were observed among PPA variants. Adjusted receiver operating characteristic analyses demonstrated high discrimination between each PPA variant and controls, with areas under the curve ranging from 0.87 to 0.97 for sentence comprehension-syntax and from 0.91 to 0.97 for sentence production. However, pairwise comparisons showed no significant differences in discriminative performance among PPA variants after correction for multiple comparisons. Both sentence comprehension-syntax and sentence production showed associations with semantic and repetition measures in addition to motor speech measures. Voxel-based morphometry showed that higher sentence comprehension-syntax scores were associated with greater grey matter volume in left frontotemporal and cingulate regions, whereas higher sentence production scores were additionally associated with greater gray matter volume in frontostriatal structures, including the caudate, putamen, and thalamus. Syntactic processing impairment in Chinese-speaking individuals with PPA extends beyond the nonfluent/agrammatic variant and is associated with distributed frontotemporal and frontostriatal regions, as well as lexical-semantic and auditory verbal short term memory mechanisms. These findings suggest that the cognitive and neural manifestations of syntactic impairment may be shaped by language-specific typological properties, underscoring the need for linguistically informed approaches to the assessment and interpretation of PPA.

Neuroimaging is becoming an increasingly prominent tool to understand intimate partner violence (IPV) and partner-inflicted brain injury (BI). Yet, by nature of IPV and partner-inflicted BI, those most affected may be the least likely to be eligible, participate, and produce usable data in neuroimaging studies. A critical question therefore remains of who is represented in neuroimaging research on IPV and related BI. Here, we examined representativeness at three major stages of neuroimaging sample construction using screening records from 352 women and a subsequent cohort of 207 enrolled women with lifetime exposure to physical IPV. At the eligibility stage, three in five women eligible for behavioral research were deemed ineligible for magnetic resonance imaging (MRI) at initial screening. At the participation stage, the 61 women who underwent resting-state functional MRI (fMRI) had more education, were more likely to be employed, and reported lower psychiatric and neurobehavioral symptom burden than enrolled women who did not undergo neuroimaging. Finally, at the quality-control stage following fMRI, motion-based exclusion disproportionately removed women who had sustained the most partner-inflicted BIs. Our findings empirically reveal that eligibility, participation, and quality control each bias the demographic, clinical, abuse, and BI burden represented in an fMRI sample, and therefore have key implications for generalizability. Measuring and reporting representation bias can help identify whose experiences are underrepresented in the evidence, and accordingly reduce the risk that IPV neuroimaging research understates the very harms it seeks to characterize.

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

Background: Whole-brain segmentation of neonatal low-field MRI is needed for regional volumetric analysis, but established segmentation systems are designed around different field strengths, contrasts, and age ranges. Methods: We first applied complete end-to-end pipelines, including FreeSurfer, FSL-based processing, Infant FreeSurfer, FastSurfer, BrainSuite, SPM12/CAT12, iBEAT v2, the dHCP structural pipeline, and M-CRIB-S, directly to 0.35T neonatal T2-weighted images. Because those systems did not produce a complete 47-region output, we next tested released end-to-end AI segmentation models, including SynthSeg, FastSurfer, FastSurferVINN, and QuickNAT. These were highly sensitive to input format, contrast, voxel geometry, and the high-field training domain. We then decomposed the task into skull stripping, registration, super-resolution, atlas propagation, and tissue refinement and tested the available components individually. Results: No complete end-to-end pipeline or released AI model delivered a usable 47-region segmentation in direct application. Stage-wise testing showed that preprocessing, reconstruction, registration, atlas propagation, and expectation-maximization each solved only part of the problem. Their final assembly produced complete 47-region outputs across the neonatal cohort. Conclusions: Direct application of established pipelines was insufficient for 0.35T neonatal T2-weighted MRI. A stage-wise combination of compatible components provided the workable solution.

Amyloid-targeting therapies (ATTs) substantially reduce amyloid PET signal and slow clinical decline in early symptomatic Alzheimer disease. As ATTs enter clinical practice, PET may assess target engagement and guide treatment management, but there is no consensus on how to measure treatment-related amyloid clearance (TRAC). We compared Centiloid quantification across pipelines and visual reads for interpreting pre- and post-ATT amyloid PET. Methods: We analyzed 18F-florbetapir or 18F-florbetaben PET from 101 patients treated with lecanemab or donanemab: 88 pre-ATT and 54 post-ATT scans, including 41 paired scans. Centiloids were derived using FDA-cleared MIMneuro and two research pipelines (rPOP, CapAIBL) and compared using intraclass correlation coefficients (ICC). Clearance rates were estimated using linear mixed-effects models. Full TRAC was defined on post-ATT scans quantitatively (<11 Centiloids) or based on visual interpretation by three expert readers. Results: Treated patients were 74+/-7 years old; 57% were female, 59% were apolipoprotein E4 carriers, and 78% had mild cognitive impairment (22% mild dementia). Centiloid agreement between pipelines was equally high pre- and post-ATT (both ICCs=0.90). Among paired scans (mean treatment duration, 16.7 +/- 4.6 months), clearance estimates were similar across pipelines and greater with donanemab (n=15 patients; between -62 and -60 Centiloids/year depending on the pipeline) than lecanemab (n=26; -38 to -36 Centiloids/year; all Ps<0.005). Among 54 post-ATT scans, between 44% and 57% were <11 Centiloids depending on pipeline, with 20 (37%) meeting this criterion across all three pipelines. In contrast, only 11% to 19% were visually negative across readers, with 4 (7%) unanimously read as negative. Of the 20 post-ATT scans <11 Centiloids across all pipelines, 13 (65%) were considered positive by all readers, commonly with residual occipital binding. Conclusion: Centiloid quantification was robust across pipelines before and after ATT initiation, yielding clearance rates consistent with clinical trials. However, quantitative and visual assessments were markedly discordant after ATT, with an unusually high proportion of scans <11 Centiloids remaining visually positive, warranting cautious interpretation of post-ATT Centiloid values. Larger studies should assess the clinical relevance of these findings and further develop robust approaches to establish full TRAC.

Importance: Brain MRIs are routinely acquired following moderate or severe traumatic brain injury (TBI) because of their high sensitivity for detecting traumatic brain lesions. However, there is no standard framework for their interpretation, and their clinical utility is unproven. Objective: Here, we test whether MRI-based quantification of traumatic brain lesions improves outcome prediction relative to the best tools currently available to clinicians. Design, Setting and Participants: We analyzed hemorrhagic contusions and axonal injury in four prospective studies of moderate or severe TBI, validating results in a fifth held-out dataset. Exposure: Contusion volume was measured via manual segmentation. Axonal injury volume was quantified with diffusion tensor imaging, by measuring the volume of skeletonized fractional anisotropy significantly lower than that of healthy controls. Main Outcomes and Measures: Functional outcomes were assessed at 6 months with the Glasgow Outcome Scale-Extended; values [&le;] 4 were considered unfavorable. Results: Among 201 patients with MRI (mean age: 41, median Glasgow Coma Scale (GCS) score on admission: 7, MRI obtained median 14 days post-injury), MRI-measured contusion volume discriminated outcomes better than CT Marshall score (AUC 0.81 vs 0.64; p=0.001), and remained associated with unfavorable outcomes controlling for multiple commonly-measured clinical variables. Quantitative axonal injury volume discriminated outcomes better than manual MRI grading (AUC 0.82 vs 0.62; p<0.001) and remained associated with unfavorable outcomes independent of other clinical predictors. The prognostic discrimination of traumatic lesion volumes was not improved by accounting for the location or normative connectivity of either lesion type. In a multivariate model, contusion and axonal injury volumes were independently predictive, with age-dependent volume thresholds for 90% probability of unfavorable outcome, and strong discrimination in an external cohort (N=27; AUC 0.83, p<0.001). Replacing all CT-based measures with MRI lesion volumes in the IMPACT model significantly improved out-of-sample prognostic performance (AUC 0.85 vs 0.64; p<0.05). Conclusions and Relevance: MRI-based lesion quantification improves prognostic discrimination relative to measures currently available to clinicians treating patients with TBI. Global functioning at 6-months is driven more by the volume than the location or connectivity of traumatic lesions. These findings support integrating MRI-based lesion quantification into clinical workflows and updated TBI classification frameworks.

Normative trajectories of resting state bipolar EEG power over the lifespan were derived from a sample of 5781 participants from the Collaborative Study on the Genetics of Alcoholism (COGA), a large multi site study, using 12,906 observations from ages 12 through 70. Trajectories were analyzed using the means, variances, and correlations of data in seven frequency bands (from low theta to high beta) and 24 bipolar channels obtained from 17 electrodes of the 10-20 system. In order to make the study more useful for understanding EEG in the general population, only subjects never having an alcohol use disorder (AUD) diagnosis were included in the study. Bipolar EEG data is derived from the differencing of the signals in adjacent electrodes, either in a lateral (left-right) or sagittal (anterior-posterior) orientation; each difference is called a channel. The power values for the individual observations were calculated by standard Fourier transform based methods. In all cases there are sharp decreases in EEG power from ages 12 to the early 20s after which there are number of divergent patterns: general increases in the beta frequencies, and mixed patterns of increase and subsequent decrease in the theta and alpha frequencies. In these characteristics the lifespan trajectories presented here are similar to the monopolar trajectories described in our previous study \citet{Cho2026}. Sagittal and lateral bipolar trajectories are similar within each sex and between sexes; the primary difference is the much lower degree of regional difference in the sagittal trajectories as compared to the lateral trajectories. The pairwise inter-frequency (within channel) correlation trajectories between the four theta and alpha frequency bands and the four high alpha and beta frequency bands at each channel show striking sex differences unlike the corresponding monopolar correlations. There are a number of features in the bipolar power trajectories that suggest that the differencing procedure reduces not only local phase synchrony, but also effects of global and regional phase synchrony present in the monopolar data from which the bipolar signals are derived. The inter-frequency correlations at the individual channels are much higher in the bipolar data than the monopolar data, suggesting that global and regional phase synchrony components retained in the monopolar data have greater frequency specificity than the more purely local components in the bipolar data. Further analyses to examine many other features of the data will be undertaken in our subsequent work.

Background: Spontaneous intracerebral hemorrhage (ICH) has high disability and mortality. Accurate early prognostication remains challenging in routine practice. Conventional CT assessment relies mainly on hematoma volume and rough anatomical location, limiting individualized prognosis. Radiomics combined with machine learning may improve prognostic assessment by extracting quantitative texture and heterogeneity features.Methods: In this retrospective multicenter study, we included 2,680 consecutive patients with spontaneous ICH: training (n=1,876), internal validation (n=804), and independent external validation (n=196). Deep learning automatically segmented hematomas, and 1,690 radiomics features were extracted from admission non-contrast CT (NCCT). After reproducibility assessment, univariate screening, and LASSO selection, 42 features were integrated into a Rad-score. The Rad-score was combined with demographic, clinical, and conventional CT variables to construct two complementary machine learning models for functional outcome prediction and comprehensive prognostic stratification. Performance was assessed using R2, mean squared error (MSE), area under the receiver operating characteristic curve (AUC), calibration, decision curve analysis, and external validation. Both models were internally and externally validated. Ambient temperature variation at symptom onset was explored as a potential prognostic modifier. Results: Forty-two reproducible radiomics features constituted the Rad-score. Adding radiomics substantially improved performance over conventional clinical and imaging models. For functional outcome prediction, the integrated model increased R2 from 0.35 to 0.93 and reduced MSE from 387 to 61. For prognostic risk stratification, the clinical-only model achieved an AUC of 0.59, whereas the fully integrated model achieved 0.95 and maintained robust external performance (AUC=0.86). Conclusions: We developed and validated a machine learning-based clinical decision support system integrating conventional CT radiomics and clinical variables for prognostic prediction and risk stratification in ICH. By capturing quantitative imaging information beyond conventional CT markers, this framework improved prognostic discrimination and generalizability. These findings support integrating quantitative imaging biomarkers into clinical decision-making for spontaneous ICH. The association between ambient temperature variation and disease progression warrants prospective investigation. Further prospective validation is warranted.

BACKGROUND: One in three adolescents with a concussion history report experiencing long-lasting somatic, sleep, emotional, and/or cognitive disturbances (persisting symptoms after concussion (PSaC)). Cognitive difficulties, such as attentional problems, are common symptoms and can interfere with ongoing development. Clinically, psychiatric symptoms also present in youth with PSaC. A concussion causes diffuse functional and microstructural alterations to neural networks. To develop targeted treatments for PSaC (e.g., neurostimulation), we need to understand how and where structural alterations and neural dysfunction intersect to cause long-lasting symptoms. OBJECTIVES: 1) Identify functional and microstructural properties in intrinsic connectivity networks, as well as neuropsychological outcomes, that differentiate adolescents with concussion with PSaC from those who do not have PSaC. 2) Determine whether neural activity within intrinsic connectivity networks mediates the relationship between network microstructure and specific neuropsychological features in adolescents with concussion. METHODS: CAMBY is a prospective study of adolescents aged 12 to 18 years with a history of concussion. Participants are allocated based on recovery status (PSaC vs. No-PSaC (recovered controls)). Advanced structural (magnetic resonance imaging; MRI) and functional (magnetoencephalography; MEG) neuroimaging and neuropsychological measures will be collected one month or more after concussion. Microstructural and functional features will be computed within intrinsic connectivity networks, then compared between PSaC and recovered control groups and related to symptom domains and neuropsychological profiles. IMPACT: This will be the first study to integrate MRI, MEG, and neuropsychology in adolescents with PSaC to identify disrupted networks underlying PSaC. This observational study is a necessary step towards future clinical trials that accurately target promising therapeutic neurostimulation protocols.

Treatment resistance in obsessive-compulsive disorder (OCD) is common, with 40-60% of patients failing to respond to pharmacotherapy. Identifying functional differences between responders and non-responders may guide alternative treatment strategies. We investigated resting-state brain differences between selective serotonin reuptake inhibitor (SSRI) responders and non-responders and compared them with healthy controls (HC). 3T MRI scans were acquired from 80 participants with OCD and 50 HCs. Structural and 5-minute resting-state data were processed using the CONN toolbox. Regions showing group differences in amplitude of low-frequency fluctuation (ALFF) were identified for seed-based connectivity analysis. ANCOVA revealed significant ALFF differences among groups in the anterior middle temporal gyrus (R-aMTG) and temporal pole (R-TP). SSRI responders showed reductions in seed-to-voxel connectivity than non-responders between these regions and the left superior and inferior occipital cortices, left superior parietal lobule, and left temporo-occipital middle temporal gyrus. Responders also showed reductions in connectivity with the left superior parietal lobule compared with HCs. Across the OCD group, Y-BOCS scores positively correlated with ALFF in the R-aMTG (p=0.008) and R-TP (p=0.044). Altered temporal cortical activity and connectivity with posterior regions were associated with SSRI treatment response, supporting a role for temporal-posterior cortical networks in OCD treatment response.

Structural connectomics provides a means of characterising white matter (WM) network organisation using diffusion MRI (dMRI), but reconstructing neonatal structural connectomes remains challenging due to the immature and heterogeneous nature of the developing brain. Although recent neonatal studies have increasingly adopted approaches beyond conventional diffusion tensor imaging (DTI), there remains scope to incorporate more advanced tractography and streamline post-processing approaches within neonatal pipelines. Here, we propose a neonatal structural connectome pipeline integrating surface-based anatomical constraints, ensemble tractography, hierarchical anatomical refinement of pathways (HARP) filtering, and spherical-deconvolution informed filtering (SIFT2) reweighting. We acquired 3T MRI data from 300 neonates (185 preterm, 115 term-born) to assess the impact of pipeline configuration on network organisation. Mean gestational age (GA) at birth was 29.31 (23.43-32.86) weeks in preterm infants and 39.52 (37.00-42.43) weeks in term infants. At scan, mean GA was 40.85 (36.29-49.00) weeks in preterm infants and 42.06 (38.29-48.00) weeks in term infants. Four connectome variants were generated: raw surfaced-based tractograms, HARP-filtered, SIFT2-weighted, and combined HARP+SIFT2. Pipeline performance was primarily evaluated using an existing brain age prediction model: connectome-based predictive model (CPM) and our derived connectomes to estimate GA at scan in term and preterm infants. In addition, global graph theoretical metric analyses were conducted to provide a complementary assessment of pipeline configuration differences in network organisation. In total, seven graph theoretical metrics were calculated and evaluated: global efficiency, local efficiency, transitivity, modularity, assortativity, degree variance and statistical complexity. Across all configurations, connectomes supported accurate brain age prediction, with mean absolute errors (MAE) of 0.76-0.79 weeks in term infants and 1.00-1.06 weeks in preterm infants. Prediction error and brain age gap variability were greater in preterm than term infants across configurations, with HARP+SIFT2 achieving the lowest preterm MAE (1.00 weeks) and brain age gap (BAG) variability (SD = 1.38 weeks). However, differences in prediction performance across configurations were minimal, suggesting limited influence of pipeline choice on brain age prediction. However, analysis of global network metrics demonstrated that pipeline choice significantly influenced downstream measures of network organisation, with HARP+SIFT2 being the only configuration to identify significant preterm vs. term differences across all seven metrics examined. These findings demonstrate the importance of considering tractography and streamline-processing choices when constructing neonatal structural connectomes and provide a framework for more anatomically informed investigation of early brain development.

Inhibitory control deficits have long been proposed as a central neurocognitive mechanism underlying childhood Attention Deficit/Hyperactivity Disorder (ADHD). However, neuroimaging findings remain heterogeneous, partly due to methodological variability across developmental samples and experimental paradigms. The present study aimed to characterize the neural architecture of motor response inhibition in typically developing (TD) children and identify reproducible functional abnormalities associated with pediatric ADHD using stringent, developmentally specific inclusion criteria. We conducted two coordinate-based meta-analyses using Activation Likelihood Estimation (ALE). Following a systematic literature search (up to August 2026), we identified 23 task-based fMRI studies reporting inhibitory-control-related activation in TD children (237 foci) and 17 studies comparing children with ADHD and TD peers (114 foci) during the Stop-Signal Task or Go/No-Go Task. All participants were younger than 18 years of age. Statistical significance was determined using cluster-level family-wise error (FWE) correction. In TD children, ALE revealed a robust inhibitory-control network encompassing the bilateral anterior insula (AI), inferior frontal gyrus (IFG), and the midline pre-supplementary motor area (preSMA) and anterior cingulate cortex (ACC). This spatial organization closely resembled the canonical inhibitory-control network consistently observed in adults, suggesting that the core neural architecture supporting motor inhibition is already detectably organized during development. In the between-group analysis, children with ADHD exhibited significant hypoactivation primarily within the preSMA. Notably, this region demonstrated striking spatial overlap with the preSMA cluster reliably recruited in TD children during motor inhibition. These findings provide convergent support for inhibitory-control models of ADHD and identify altered preSMA recruitment as one of the most consistent functional abnormalities associated with pediatric ADHD. While the neural infrastructure supporting inhibitory control is broadly established during childhood, children with ADHD exhibit reduced recruitment of a key cortical node within this network during motor inhibition demands.

Modest positive associations between episodic memory performance and whole hippocampal and hippocampal subfield volumes have been reported in numerous prior studies. A smaller number of studies have reported associations between hippocampal volume and performance on tests of non-mnemonic cognition. The present study examined whether these associations were evident in a lifespan sample of cognitively healthy adults. Of particular interest was whether any identified associations were sensitive to age, and whether associations between subfield volumes and mnemonic and non-mnemonic performance were subfield dependent. We acquired high-resolution T1- and T2-weighted structural images from 163 adults (18-87 years of age). Participants also undertook a comprehensive neuropsychological test battery and an in-scanner test of source memory. Principal components analysis was employed to reduce the neuropsychological test scores to 5 cognitive components. Two components reflected memory performance while the other three reflected different aspects of non-mnemonic cognition. Hippocampal subfields (Cornu Ammonis (CA)1, CA2-3, dentate gyrus (DG) and subiculum) were segmented and measured with the Automated Segmentation of Hippocampus Subfields (ASHS) package. Source memory performance was selectively associated across participants with CA2-3 volume. By contrast, both mnemonic and non-mnemonic component scores derived from the test battery were associated exclusively with the volume of the DG. All associations were age-invariant. The findings indicate that different cognitive domains can be dissociated by virtue of their associations with different hippocampal subfields. Of importance, these associations appear to be life-long and hence are unlikely to reflect individual differences in age-related decline in structural integrity.