Persistent debate surrounds whether the frontal lobe supports the emergence or reporting of consciousness, raising the hypothesis that distinct frontal subregions may support these processes. We addressed this by combining electroencephalography (EEG) with eye-tracking in Report and No-Report paradigms. Eye-movement features distinguished conscious and unconscious trials in the no-report task. Event-related potential analyses showed that the Visual Awareness Negativity (VAN) was independent of reporting, whereas P3b occurred only with explicit reports. Importantly, the frontal Dorsal Attention Network (DAN) supported the emergence of consciousness, independent of post-perceptual reporting, as shown by multivoxel pattern analysis showing that a classifier's ability to decode visual consciousness generalized bidirectionally between report and no-report tasks. In contrast, frontal components of the Default Mode Network (DMN) and Frontoparietal Control Network (FPN) encoded visual consciousness only when explicit reports were required, indicating roles in reporting. These findings demonstrate a functional dissociation within the frontal lobe and refine the anatomical framework for the neural basis of visual consciousness.
Research efforts spanning more than seven decades have used functional neuroimaging to investigate whether putative extra‑sensory perception (ESP/receptive psi) has identifiable neural correlates. However, the field lacks a coherent and critical synthesis of its methodological approaches and reported effects. We conducted a systematic review consolidating 143 reports and qualitatively evaluated the methods of 129 individual studies. We organized studies by paradigm using two broad categories: (1) explicit psi paradigms (including forced-choice and free-response design subcategories), in which psi is assessed via overt responses; and (2) implicit psi paradigms (including distant stimulation, distant intentionality, and predictive anticipatory activity subcategories), in which psi is assessed solely via neurophysiology. Most work relied on EEG (91%), followed by fMRI (5%). We identified recurrent methodological limitations, in particular, small sample sizes, inadequate multiple‑comparison control, and analytical flexibility. Explicit paradigms rarely showed above‑chance behavior, yet investigators frequently proceeded to neural analyses, implying unacknowledged shifts in the operational definition of psi. Implicit paradigms often reported psi‑consistent effects, but findings were heterogeneous and seldom replicated. Overall, definitive conclusions about the neural correlates of ESP remain premature. Nevertheless, we identify potential leads-such as alpha‑band power in forced-choice designs, and a target‑related negative slow wave in event‑related designs-as testable candidates for future research. We provide a set of 13 methodological recommendations to promote cumulative progress, as well as 6 recommendations for future research directions.
To develop a semi-automated method to segment "black hole" lesions on post-gadolinium 2D T1-weighted images (GdT1) in multiple sclerosis (MS) that follows radiological intensity rules and perform multi-center validation. Multi-center spin-echo GdT1 images and accompanying proton-density (PD)/T2-weighted images and manual T2 lesion masks of the REFLEXION study (NCT00813709) of suspected/early MS were used. Briefly, the proposed method segments cortical gray matter (GM) to derive a T1-weighted intensity threshold, which is applied inside co-registered T2 lesion masks to segment black hole lesion voxels. It was optimized on a training set (N = 40, 57.5% female, mean age 31.4 ± 8.7 (standard deviation) years), and 274 patients formed the test set (61.3% female, age 31.8 ± 8.4 years). Performance was quantified by the Dice similarity coefficient (DSC) and the intraclass correlation coefficient (ICC) for absolute agreement with manual segmentations. Lesion-wise sensitivity and specificity were calculated. Optimization resulted in: (1) GM selection as minimally 0.8 total WM plus GM partial volume, masked by MNI cortex; (2) normalized mutual information-driven linear co-registration of T2 to GdT1 images, interpolating T2 lesion masks using trilinear interpolation and 0.6 threshold; (3) mean intensity inside GM mask used as upper intensity threshold. The optimized method had acceptable spatial accuracy (DSC: 0.39 ± 0.26) and good volumetric accuracy (ICC: 0.84, 95% CI [0.72, 0.90]. Lesion-wise sensitivity was 0.91 ± 0.19, and lesion-wise specificity was 0.62 ± 0.22. The proposed method to semi-automatically segment black holes from post-gadolinium T1-weighted images shows acceptable performance. As a potential aid to radiologists, the method is not recommended to be used entirely without human intervention. Question T1-hypointense "black hole" lesions reflect disease severity in multiple sclerosis but are not routinely quantified due to a lack of reliable analysis methods. Findings A rule-based semi-automated method for GdT1 "black hole" lesion segmentation was developed and optimized, and then validated in a large unseen multi-center test set. Clinical relevance This method adds quantitative information about GdT1 "black hole" lesions to the radiological assessment of multiple sclerosis disease severity, when false positives are manually removed. This can enhance the characterization of individual patients and advance the understanding of the disease.
Habituation reduces the salience of repeated socio-emotional cues like facial expressions once they become familiar, predictable, or no longer relevant. To clarify whether variation in amygdala habituation to repeated emotional faces is associated with psychological symptoms and persistent antisocial behavior, the current study examined whether internalizing and externalizing symptoms, and their interaction, were related to amygdala habituation in treatment-referred young adults with antisocial histories, and whether habituation predicted future recidivism. For this purpose, ninety-eight treatment-referred young adults (18-27) with a history of antisocial behavior performed an emotional face-matching fMRI task presenting fearful, angry, sad, happy and neutral faces. Habituation was operationalized as the change in amygdala activation from the first to the second half of the task for each emotional face condition. Internalizing and externalizing symptoms were measured with the Adult Self-Report. Recidivism was obtained from Dutch national judicial records (median follow-up 2.5 years post-scan). Amygdala habituation differed by emotional expression in the treatment-referred youth, with the strongest habituation for happy faces and relatively weaker habituation for negative and neutral expressions. However, within this group, individual differences in habituation were not associated with internalizing symptoms, externalizing symptoms, their interaction or recidivism. Additional analyses comparing treatment-referred youth with healthy controls indicated that treatment-referred young adults showed reduced habituation to fearful faces compared to controls. Taken together, these findings suggest that amygdala habituation may help identify emotion-specific neural processing differences between treatment-referred young adults and controls, but is not associated with dimensional symptom heterogeneity or future recidivism within treatment-referred young adults.
Pituitary adenomas represent one of the most common intracranial tumors, and cavernous sinus invasion (CSI remains a major challenge for surgical management. Although the Knosp grading system provides a widely used radiological framework, its subjective nature and inter-observer variability limit diagnostic reliability. In recent years, advanced computational methods have been investigated to improve the preoperative prediction of invasion. This review synthesizes current evidence on the use of radiomics, machine learning (ML), and deep learning (DL) approaches in the detection and assessment of CSI in pituitary adenomas, with particular emphasis on their comparative performance against traditional imaging methods. Studies employing MRI-based radiomic feature extraction, ML classifiers, and convolutional neural networks were analyzed. Reported models commonly incorporated intensity, texture, and shape descriptors, or applied end-to-end DL architectures for automated prediction. Performance metrics such as accuracy, sensitivity, specificity, AUC, and Dice similarity coefficients were compared across studies, with Knosp grade serving as a frequent benchmark. Evidence suggests that ML and DL models consistently outperform conventional MRI interpretation in predicting CSI. Radiomics pipelines integrating quantitative imaging features with clinical variables achieved high diagnostic accuracy, while CNN-based models trained on contrast-enhanced MRI often exceeded AUC values of 0.85. Furthermore, automated segmentation frameworks demonstrated reliable delineation of tumor boundaries, facilitating improved assessment of invasive behavior. Despite promising outcomes, limitations such as small sample sizes, single-center designs, and lack of external validation restrict broad clinical adoption. Radiomics and AI-driven approaches show substantial potential for enhancing preoperative evaluation of pituitary adenomas with CSI. Standardized imaging protocols, multicenter collaborations, and transparent model validation are essential for future integration into neurosurgical decision-making.
Transcranial direct current stimulation (tDCS) has shown encouraging results in patients with Disorders of Consciousness (DoC), yet its efficacy and mechanisms remain unclear. This study aimed to evaluate the effects of left dorsolateral prefrontal cortex (DLPFC) tDCS on Coma Recovery Scale-Revised (CRS-R) scores and EEG activity in severely brain-injured DoC patients. In a double-blind, sham-controlled crossover design, 2 mA anodal and sham tDCS were randomly administered over the left DLPFC (right orbito-frontal cathode) for 20 min. CRS-R scores, event-related potentials using the local-global paradigm and resting EEG were recorded before and after each stimulation. Follow-up assessments occurred at 6 weeks and 6 months. Between November 2018 and April 2022, 20 patients were included (12 in a Minimally Conscious State (MCS), 7 in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS), and one patient who emerged from MCS (emergence from MCS (eMCS)). Group-level analysis across all patients revealed a significant treatment effect on CRS-R scores (p = 0.02). Nine patients (45%; 6 MCS, 3 VS/UWS) responded to tDCS, with five (25%; 3 MCS, 2 VS/UWS) exhibiting new behavioural items absent in pre- and post-sham evaluations. However, no patient demonstrated a change in consciousness state (e.g., transition from VS/UWS to MCS), nor was any improvement observed at 6-week or 6-month. EEG analysis showed increased complexity, gamma-band power, and delta-band cortico-cortical functional connectivity post-tDCS. Multivariate EEG analyses did not show a significant effect of tDCS on the prediction of conscious state, and ERP assessments using the local-global paradigm revealed no modulation of novelty detection responses following stimulation. Simulation analyses on individual brain anatomical structures revealed that responders displayed stronger tDCS-induced electric fields within a Fronto-parietal network than non-responders. Despite the limited sample size, our results provide additional evidence in favour of the behavioural and electrophysiological effects of tDCS in DoC patients.
Parkinson's disease (PD) is a neurodegenerative disease characterised by molecular and structural brain changes detectable through advanced imaging. Understanding alterations in neurotransmitter systems and synaptic density, and their clinical relevance, is critical for identifying disease-specific biomarkers and therapeutic targets. This study included 33 PD patients (27 idiopathic PD (iPD) and 6 LRRK2 mutation carriers) (5.2 ± 3.6 years from diagnosis, 2.1 ± 0.7 Hoehn & Yahr OFF state) and 25 healthy controls (HC). Longitudinal data were collected for 20 iPD and 22 HC (10-33 months post-baseline; 20.2 ± 7.3 months). Participants underwent clinical assessments, structural magnetic resonance imaging, 11C-UCB-J positron emission tomography (PET) to assess synaptic density, 11C-DASB PET to assess serotonin transporter density, and 123I-FP-CIT single-photon emission computed tomography to assess dopamine transporter density. Analyses included baseline group comparisons, clinical correlations, and longitudinal assessments. At baseline, lower 123I-FP-CIT uptake in caudate and putamen (p < 0.001) and reduced 11C-DASB binding in the insular cortex (p = 0.003), parietal lobe (p = 0.009), caudate (p < 0.001), and putamen (p = 0.002) were observed in PD compared to HC. Some baseline correlations emerged between imaging metrics and symptom scales in PD, though these were limited. Despite progression in motor impairment, autonomic dysfunction, and overall disability in PD, no significant longitudinal changes or group × time interactions were detected for molecular imaging measures. This study confirmed dopaminergic and serotonergic dysfunction in PD. Synaptic density did not differ between groups or change over time, suggesting synaptic loss may be minimal at mild-to-moderate disease stages. These findings highlight how different molecular imaging markers reflect distinct aspects and timescales of PD pathophysiology.
In the European Union (EU), donanemab is indicated in adults with early symptomatic Alzheimer's disease who are apolipoprotein E ε4 non-carriers or heterozygotes. Among these, patients without superficial siderosis at baseline, uncontrolled hypertension, or anticoagulant use are eligible. To assess efficacy and safety of donanemab in the EU-eligible population. A post-hoc conservative hybrid imputation method was implemented for clinical efficacy analyses during the TRAILBLAZER-ALZ 2 placebo-controlled period. In the 78-week long-term extension (LTE) participants in the early-start (randomised to donanemab) and delayed-start (randomised to placebo with donanemab initiation during the LTE) groups were compared to a propensity-weighted external control. Participants were switched to placebo after meeting amyloid-based treatment course completion criteria. By 76 weeks, donanemab-treated participants in the EU-eligible population had a mean Clinical Dementia Rating Scale (CDR)-Sum of Boxes change from baseline difference from placebo of -0.7 points (95% confidence interval, -1.0, -0.4) and a 40.3% lower risk of disease progression to the next stage (per CDR-Global score). Treatment benefit increased over 154 weeks for non-carriers and heterozygotes, including those meeting treatment course completion criteria by 52 or 76 weeks. In the placebo-controlled period, 119 (19.5%) and 49 (8.0%) donanemab-treated eligible participants experienced amyloid-related imaging abnormalities-edema/effusion and infusion-related reactions, respectively. Safety findings were similar among donanemab-treated participants in the placebo-controlled period and LTE delayed-start group. Consistent with previous TRAILBLAZER-ALZ 2 and LTE findings, donanemab significantly slowed disease progression compared to controls with a manageable safety profile in non-carriers and heterozygotes.
Musical improvisation requires complex cognitive operations, including idea generation, retrieval, and evaluation, supported by continuous sensory feedback. The neural reorganization that accompanies sensory loss has been described, but its impact on improvisation remains poorly understood. We present a case study of Matthew Whitaker (MW), a blind jazz piano prodigy with retinopathy of prematurity, to examine how cortical plasticity supports musical expertise. Functional magnetic resonance imaging (fMRI) was used to probe neural activity during three paradigms: music perception, proprioceptive sound-to-space mapping, and improvisation. As no suitable control group exists for a blind musical prodigy, internal controls contrasted experimental conditions to a matched control task for each paradigm. Results revealed recruitment of occipital visual regions during music perception, engagement of fusiform face areas during proprioceptive keyboard navigation, and coordinated activation and deactivation of frontal and occipital regions during improvisation. These findings demonstrate extensive cross-modal plasticity. As a musician who is blind, MW has undergone functional neural reorganization, recruiting his unused visual areas to aid him in his musical pursuits, from listening to navigating the keyboard to improvising.
Distracted eating is prevalent in modern environments. While behavioral research consistently shows that distraction attenuates taste perception and increases food intake, the underlying neural mechanisms appear to be more complex. This functional magnetic resonance imaging (fMRI) study investigated whether naturalistic distraction modulates gustatory processing via sensory suppression or reallocation of neural resources, as observed in more controlled cognitive load paradigms. Thirty-eight healthy participants received sweet and umami taste stimuli of low and high concentration during fMRI scanning. Attentional state was manipulated using short food-related (low-distraction) versus film-related (high-distraction) video clips. After each video, participants rated perceived intensity and pleasantness. Group-level analyses included covariates for sex, body mass index (BMI), and hunger level. High distraction attenuated perceived intensity (p < 0.001, d = -0.28) and pleasantness (p < 0.01, d = -0.21), independent of taste category or concentration. No significant attenuation by distraction was observed in core gustatory regions (insula, orbitofrontal cortex). Instead, distraction increased activation in occipital, thalamic, and cerebellar regions, indicating a redistribution of processing resources toward visual and attentional systems. Distraction reduced taste salience without lowering gustatory cortex activity, supporting resource-competition models rather than active sensory suppression. These results reinforce that the impact of distracted eating is behaviorally robust yet neurally subtle, highlighting the need for personalized stimuli and ecologically valid methods to capture real-world eating behavior. The study demonstrates that video-based paradigms work reliably in fMRI and capture how naturalistic distraction alters taste experience.
In the brain, vasomotor dynamics at infra-slow frequencies (∼0.1 Hz), driven by synchronized oscillations of smooth muscle cells in vessel walls, are thought to play a crucial role in regulating cerebral perfusion and underlie resting-state functional connectivity (FC), typically measured by correlated time courses of functional signals. In particular, rodent studies have demonstrated that vasomotor activity contributes to the coherence of blood oxygenation level dependent (BOLD) signal fluctuations. However, in humans, detecting this contribution non-invasively remains challenging due to the limited spatiotemporal sensitivity of functional magnetic resonance imaging (fMRI) to vasomotion. Given that prior studies have identified internal carotid artery stenosis (ICAS) as an informative conditional lesion model of vasomotor and hemodynamic impairments in humans, we investigated whether ICAS affects interhemispheric BOLD coherence at ∼0.1 Hz. Using a multi-modal fMRI framework integrating resting-state fMRI with quantitative mapping of cerebral blood volume, blood flow, oxygen metabolism, and BOLD time lag, we compared BOLD coherence between patients with asymptomatic unilateral ICAS and healthy controls. Frequency-specific analysis revealed significantly diminished inter-hemispheric BOLD coherence at ∼0.1 Hz across canonical resting-state networks in ICAS patients, while ultra-slow (<0.05 Hz) coherence remained largely preserved. This reduction was spatially widespread across brain networks and particularly pronounced in watershed areas, i.e., border zones between major vascular territories, associated with significantly increased lateralization of cerebral blood volume (p < 0.01). Notably, coherence-based FC patterns at ∼0.1 Hz were heterogeneous within watershed areas but homogeneous outside, suggesting an interplay between compensatory mechanisms and cerebrovascular impairment. Taken together, our findings demonstrate that ICAS induces subtle, frequency- and region-specific alterations in interhemispheric FC, consistent with a model in which impaired vasomotor activity and hemodynamic dysfunctions impact resting-state FC in the human brain.
Mild cognitive impairment (MCI) represents anearly stage of cognitive decline, often preceding dementia. While hippocampal atrophy is a recognized imaging marker, the role of deep gray matter structures and tissue alterations remains less understood. This study aimed to assess whether quantitative T1 (qT1) relaxometry and volumetric measures derived from synthetic MRI are associated with global cognitive performance, as measured by the Montreal Cognitive Assessment (MoCA). In this cross-sectional study, 74 healthy adults (mean age 43.7 years, 50% female) underwent 3D-synthetic MRI and MoCA testing. Participants were stratified into cognitively normal (MoCA ≥26) and MCI (MoCA <26) groups. Brain volumes and qT1 values were extracted across regions. Associations with MoCA were assessed using multivariate regression, controlling for demographic and clinical covariates. Left hippocampal volume was significantly reduced in the MCI group (p = 0.019) and was positively associated with MoCA scores (R2 = 0.18). Although qT1 values did not differ significantly between groups, qT1 in the right putamen independently predicted MoCA scores when adjusted for age (combined model R2 = 0.22). Age was also associated with cortical gray matter qT1 (r = -0.33, p = 0.005) and increased CSF volume (r = 0.45, p < 0.0001), indicating age-related structural changes. No significant effects of sex, BMI, vascular risk, or comorbidities were observed. Hippocampal volume and putamen qT1 are independent imaging correlates of cognitive performance. qT1 mapping may detect early subtle tissue changes not captured by conventional volumetry, supporting its potential role as a biomarker in cognitive aging.
Traumatic brain injury (TBI) represents a major cause of chronic disability worldwide, with outcomes that are notoriously heterogeneous and difficult to predict. This heterogeneity stems from the fact that TBI is not merely a focal injury but a whole-brain network disorder. The brain's structural and functional organization-the connectome-is profoundly disrupted by traumatic forces. However, the post-injury period is not static; it is characterized by a dynamic, multi-stage process of reorganization that unfolds from hours to years. This review synthesizes current evidence on how neurotrauma dismantles brain networks and how the brain subsequently attempts to repair and reconfigure itself. We explore the cellular and systems-level mechanisms of neuroplasticity, the advanced neuroimaging techniques used to measure network changes, and the critical distinction between adaptive and maladaptive reorganization. Finally, we discuss the therapeutic implications of this network-oriented perspective, highlighting neuromodulation, cognitive rehabilitation, and pharmacological interventions as tools to guide recovery, and conclude by outlining key future directions for the field.
Infantile hydrocephalus (IH) can lead to lasting brain structure changes despite early treatment. We investigated deep gray matter morphology in children with treated IH using volumetric analysis and 3D shape modeling. Twenty-one IH patients (diagnosed and treated within the first 2 years of life) and 21 age- and sex-matched controls underwent 3T MRI. We measured volumes of five bilateral subcortical structures (caudate, thalamus, putamen, pallidum, hippocampus) and performed shape analysis with spherical harmonic point distribution models (SPHARM-PDM) to map regional surface deformations. Group differences were assessed by t-tests for volume and vertex-wise general linear models for shape (false discovery rate q < 0.05). IH patients had significantly smaller volumes than controls in all examined structures (p < 0.001). Shape analysis revealed extensive localized differences in the caudate, thalamus, putamen, and pallidum. In IH, the surfaces adjacent to the enlarged ventricles bulged outward, while more distal parts showed inward compression (p < 0.05, corrected). The caudate head lateral surface was displaced outward in IH, whereas the caudate tail was medially compressed. The thalamus and pallidum similarly showed lateral expansion anteriorly and medial inward deformation posteriorly. The hippocampus exhibited a ∼25% volume reduction in IH but no significant regional shape differences. Early hydrocephalus results in persistent atrophy and shape distortion of deep gray matter structures. This is the first application of shape analysis in IH, revealing region-specific deformations consistent with mechanical stretching and compression from ventricular expansion. These findings underscore that measures of parenchymal integrity provide a more direct marker of hydrocephalus-related brain injury than ventricular size alone.
Adjuvant therapies in aphasia rehabilitation may help reduce the cost and clinical resources required for intensive speech-language interventions. We conducted a proof-of-principle pilot study to evaluate whether transcranial direct current stimulation (tDCS) paired with a shortened course of phonomotor treatment (sPMT)-targeting sounds and nonwords but not real words-enhances phonological production in chronic post-stroke aphasia, and to examine the neural mechanisms underlying this approach. Using a double-blind, parallel-group design, participants received 30 h of sPMT combined with 1 mA tDCS (anode/cathode over left/right inferior frontal gyrus) delivered at the start of each intervention session. Here, we report data from six older male participants matched on aphasia severity (active: n = 3; sham: n = 3). Phonological production and confrontation naming were assessed at baseline, immediately post-intervention, and at 3-month follow-up. Structural and resting-state functional MRI (rs-fMRI) were acquired at baseline and post-intervention. Repeated-measures ANOVA revealed a significant group-by-time interaction for phonological production, with the active tDCS + sPMT group showing gains from baseline to post-intervention that were maintained at 3 months, whereas the sham group showed no significant improvement. Confrontation naming showed no significant effects of time or group. MRI-based estimates of current density (J) indicated that J varied systematically with lesion volume and inter-electrode distance, underscoring the importance of individualized electrode placement. rs-fMRI analyses demonstrated significant group-by-time interactions, with greater connectivity increases in the active versus sham group across domain-specific and domain-general networks. These preliminary findings suggest that active tDCS may enhance the effects of sPMT on phonological production and provide mechanistic support for individualized, network-informed neuromodulatory approaches in post-stroke aphasia rehabilitation.
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Self-reported pain symptoms are highly prevalent during adolescence and are associated with significant individual and societal burden; yet their neurobiological correlates remain insufficiently characterized, particularly in large, community-based cohorts. Identifying patterns in resting-state functional connectivity associated with adolescent pain characteristics may elucidate neural factors relevant to future pain and related health risks. The present cross-sectional study utilized linear mixed-effects models to examine associations between resting-state functional connectivity and past-month pain characteristics (average and worst pain intensity, pain-related limitations, daily pain duration, and number of pain body regions) in youth from Year 2 of the Adolescent Brain Cognitive Development Study (ABCD). No significant differences in connectivity were observed between youth with (n = 2,364, 47.7% female, average age = 12.04) or without (n = 4,447, 46.4% female, average age = 11.99) past-month pain. However, among youth reporting pain, higher average pain intensity was associated with lower connectivity between the frontoparietal network and sensorimotor hand, sensorimotor mouth, and auditory networks, as well as greater connectivity between auditory and visual networks, and between frontoparietal and cinguloparietal networks (FDR-corrected p < 0.01). Higher worst pain intensity showed a similar pattern. No significant associations were observed for pain-related limitations, daily pain duration, or number of pain body regions. Together, these findings highlight altered connectivity between frontoparietal and distributed sensory networks as a potential neural marker of higher pain intensity.
Lateral ventricle and hippocampal volume are clinical indicators for many disorders. Manual tracing is time-consuming; the alternative, automated tracing, may be inconsistent across software programs. The purpose was to estimate the reliability of three automated measuring programs relative to manual tracing, and to consider the use of linear inter-conversion factors. Participants with mild traumatic brain injury (mTBI) were measured for lateral ventricle volume (LVV; n = 20; 35% male; mean age = 36.5) or hippocampal volume (HV; n = 50; 42% male; mean age = 39.1). Axial 4 mm T2 images and 1.2 mm coronal T1 Images were used for manual lateral ventricle tracings and hippocampal tracings, respectively (InteleViewer®). 3T MR images 1.2 mm sagittal T1 sequences were used for automated volumetric tracings in NeuroQuant® v3.0, FreeSurfer® v6, and volBrain software. Reliability was estimated by intraclass correlation coefficients (ICCs), and relationships between software programs were described using regression equations. Human inter-rater reliability was excellent (ICC = 0.99) for LVV and moderate (0.67) for HV. For LVV, software programs had excellent consistency with manual tracing and with each other (≥0.97). There was excellent agreement between manual tracing and NeuroQuant® (0.97), but moderate-good agreement (0.54-0.87) with FreeSurfer® or VolBrain, with notably size-biased underestimation of LVV. For HV, FreeSurfer® and VolBrain had excellent consistency with manual tracing (≥0.90); NeuroQuant® had moderate-good consistency with other methods (0.57-0.79). FreeSurfer® had excellent agreement with manual tracing (0.91), but poor-good agreement with other software (0.42-0.78). Compared to manual tracing, FreeSurfer® overestimated large HV, and volBrain underestimated small HV. Conversions involving HV may be ill-advised given their low consistency. Algorithms for brain volumetrics must be evaluated for distorting effects of size-associated bias.
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder linked to an increased risk for neurodegeneration and cognitive impairment. The current study set out to explore a wide range of indirect markers of neuronal function via proton magnetic resonance spectroscopy (1H-MRS) to help elucidate the link between altered glucose metabolism and neurodegeneration. Adults with T2DM (n = 20) and age- and sex-matched control subjects (n = 20) underwent fasted blood sampling, Montreal Cognitive Assessment, and 1H-MRS using a novel sequence HERCULES, allowing the reliable quantification of small and overlapping signals, adding to the number of quantifiable metabolites. Significant neurometabolic differences were observed in three brain regions. Namely, N-acetylaspartate (tNAA) and total choline (tCho) in the medial prefrontal cortex, total creatine (tCr) in the posterior cingulate cortex, and glutathione (GSH) in the hippocampus were lower in the T2D group than the control group. Glycated hemoglobin was inversely correlated with prefrontal tCho, tNAA, and tCr levels, as well as posterior cingulate tCr. In contrast, glycated hemoglobin was positively correlated with prefrontal concentrations of glutamate, along with left sensorimotor cortex glutamate, glutamine, myo-inositol, and lactate. The region-specific metabolic deficits in tNAA, tCho, tCr, and GSH observed in the default mode network add to our understanding of diabetic encephalopathy. These exploratory findings might support a deficit model of brain energy metabolism and raise clinically relevant research questions about the neuro-energetic underpinnings of cognitive impairment in T2DM.
The deep gray matter nuclei (DGMN)-including the thalamus, caudate, putamen, and pallidum-are essential for cognitive, motor, and affective functions and are highly susceptible to age-related degeneration. However, the combined effects of aging and sex on DGMN volume, microstructure, and perfusion remain incompletely characterized. Using data from 652 healthy adults (36-89 years) from the Human Connectome Project-Aging cohort, we examined volumetric and perfusion changes across the DGMN. High-resolution T1- and T2-weighted MRI and multi-delay pseudo-continuous arterial spin labeling (pCASL) were used to quantify normalized volume, T1/T2 ratio, cerebral blood flow (CBF), and arterial transit time (ATT) in DGMN regions after perivascular spaces were removed. Age-related trajectories were modeled using linear and quadratic regression, and differences between sexes were examined, with false discovery rate correction applied. Advancing age was associated with significant volumetric decline in all DGMN regions (p < 0.001), prolonged ATT, and reduced CBF, particularly in the caudate and thalamus. The T1/T2 ratio exhibited region-specific nonlinear trajectories, peaking in mid-adulthood and declining thereafter, reflecting its underlying age-related processes of demyelination and iron accumulation. Sex-stratified analyses suggested modest differences in T1/T2, ATT, and CBF trajectories; however, no significant age × sex interactions were observed after correction for multiple comparisons. DGMN volumes correlated negatively with ATT, while T1/T2 ratio correlated inversely with CBF, indicating more complex interactions between structure, tissue properties, and perfusion. This large-scale quantitative MRI study delineates distinct age-related trajectories of DGMN over adult lifespan. Integrating volumetric, T1/T2 ratio, and multi-delay ASL metrics-while correcting for perivascular spaces-enhances sensitivity to subtle changes and provides normative benchmarks for detecting early neurodegenerative alterations.