Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability

Single-dose methylphenidate induces shift in functional connectivity associated with positive longer term clinical response in adult attention-deficit/hyperactivity disorder

Stimulants, such as methylphenidate (MPH), are beneficial for attention-deficit/hyperactivity disorder (ADHD), but individual response varies. A deeper understanding of the mechanisms underpinning response is needed. Previous studies suggest that a single MPH dose modulates resting-state functional connectivity (rs-fc). We investigated whether single-dose induced rs-fc changes were associated with post-dose optimization clinical response. Fifty-six adults with ADHD underwent rs-functional magnetic resonance imaging (rs-fMRI) under placebo and a single MPH dose, before starting MPH treatment. Clinical response was measured at two months. We tested if a single MPH dose (vs. placebo) shifted rs-fc; how these shifts were associated with treatment response (categorical approach); and whether these associations were driven by improvement on either ADHD symptom domain. A single MPH dose (vs. placebo) increased rs-fc in three subcortical-cortical and cerebellar-cortical clusters. Enhanced rs-fc between the cerebellar vermis (lobule 6) and the left precentral gyrus was associated with a greater probability of responding to treatment (χ2(7) = 22.740, p = .002) and with an improvement on both inattentive and hyperactive/impulsive symptoms (both p ≤ .001). We provide proof-of-concept that the brain functional response to a single MPH dose, administered before starting routine treatment, is indicative of two-month clinical response in adult ADHD. This may encourage future replication using clinically applicable measures.

Neurostimulation improves reading and alters communication within reading networks in dyslexia

The left temporo-parietal cortex (TPC) is critical for phonological decoding during reading and appears hypoactive in dyslexia. Therefore, a promising approach to alleviating phonological deficits in dyslexia is to modulate left TPC functioning. However, it is unclear how neurostimulation alters activity and network interactions in dyslexia. To address this gap, we combined facilitatory transcranial magnetic stimulation (TMS) to the left TPC in adults with dyslexia with an overt word and pseudoword reading task during functional neuroimaging. We found TMS-induced improvements in pseudoword reading, reduced contributions of right-hemispheric regions during reading, and substantial changes between the core reading nodes and an extended network involving the right cerebellum. Stronger coupling between temporo-occipital and frontal cortices was further directly linked to improvements in pseudoword reading. Collectively, we provide evidence for a crucial role of the left TPC for phonological decoding and show that TMS can successfully modulate reading networks to improve reading in dyslexia.

Relating Functional Connectivity and Alcohol Use Disorder: A Systematic Review and Derivation of Relevance Maps for Regions and Connections

Alcohol Use Disorder (AUD), a prevalent and potentially severe psychiatric condition, is one of the leading causes of morbidity and mortality. This systematic review investigates the relationship between AUD and resting-state functional connectivity (rsFC) derived from functional magnetic resonance imaging data. Following the PRISMA guidelines, a comprehensive search yielded 248 papers, and a screening process identified 39 studies with 73 relevant analyses. Using the automated anatomical labeling atlas for whole-brain parcellation, relevance maps were generated to quantify associations between brain regions and their connections with AUD. These outcomes are based on the frequency with which significant findings are reported in the literature, to deal with the challenge of methodological diversity between analyses, including sample sizes, types of independent rsFC features, and AUD measures. The analysis focuses on whole-brain studies to mitigate selection biases associated with seed-based approaches. The most frequently reported regions include the middle and superior frontal gyri, the anterior cingulate cortex, and the insula. The generated relevance maps can serve as a valuable tool for formulating hypotheses and advancing our understanding of AUD's neural correlates in the future. This work also provides a template on how to quantitatively summarize a diverse literature, which could be applied to more specific aspects of AUD, including craving, relapse, binge drinking, or other diseases.

Sex differences in dorsal striatal volume and interest in quitting smoking

AIMS

Over the recent decades, smoking among women has become an increasingly pressing public health challenge. Mounting evidence suggests that, compared to men, women's smoking is more strongly influenced by habitual responses to sensorimotor cues. To understand the brain mechanisms underlying the cessation challenges commonly reported by women who smoke, the present study used voxel-based morphometry (VBM) to investigate sex-related volumetric differences in the dorsal striatum, a region implicated in habitual substance use behavior, and their associations with self-reported quit interest among daily smoking adults.

METHODS

Structural magnetic resonance imaging (MRI) data were collected from 41 women and 52 men (30.1 ± 7.5 years) who reported smoking an average of 15-40 cigarettes per day for at least past 24 months. Multiple regression analyses were carried out with sex and average gray matter volumes (GMV) of predetermined brain regions of interest (ROIs; bilateral caudate, putamen) and control regions (bilateral nucleus accumbens, thalamus) as predictors of self-reported interest in quitting smoking.

FINDINGS

Women displayed greater striatal GMV and lower current quit interest than men. ROI-based analyses revealed an interaction between sex and putamen GMV, wherein putamen GMV was more strongly and negatively linked to quit interest in women than men.

CONCLUSIONS

Greater GMV in the putamen could be linked to an attenuated desire to stop smoking among women. This may serve as a neuroanatomical mechanism underlying the higher prevalence of habit-driven smoking behavior observed in women as compared to men.

Enhancing MRI radiomics feature reproducibility and classification performance in Parkinson's disease: a harmonization approach to gray-level discretization variability

OBJECTIVE

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple gray-level discretization levels for classifying Parkinson's disease (PD) subtypes, and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance.

METHODS

T1-weighted MRI scans from 140 PD patients (70 tremor-dominant, 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's progression markers initiative (PPMI) database. Radiomic features were extracted from 16 brain regions using 6 discretization levels (8, 16, 32, 64, 128, and 256 bins). ComBat harmonization was applied using a combined batch variable incorporating both scanner models and discretization levels. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Support vector machine classifiers were used for PD subtype classification.

RESULTS

ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased substantially after harmonization. The proportion of features significantly affected by discretization levels was reduced following harmonization. Classification accuracy improved dramatically, from a range of 0.42-0.49 before harmonization to 0.86-0.96 after harmonization across most discretization levels. AUC values similarly increased from 0.60-0.67 to 0.93-0.99 after harmonization.

CONCLUSIONS

ComBat harmonization significantly enhanced the reproducibility of radiomic features across discretization levels and improved PD subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Comparison of modelled diffusion-derived electrical conductivities found using magnetic resonance imaging

Introduction

Magnetic resonance-based electrical conductivity imaging offers a promising new contrast mechanism to enhance disease diagnosis. Conductivity tensor imaging (CTI) combines data from MR diffusion microstructure imaging to reconstruct electrodeless low-frequency conductivity images. However, different microstructure imaging methods rely on varying diffusion models and parameters, leading to divergent tissue conductivity estimates. This study investigates the variability in conductivity predictions across different microstructure models and evaluates their alignment with experimental observations.

Methods

We used publicly available diffusion databases from neurotypical adults to extract microstructure parameters for three diffusion-based brain models: Neurite Orientation Dispersion and Density Imaging (NODDI), Soma and Neurite Density Imaging (SANDI), and Spherical Mean technique (SMT) conductivity predictions were calculated for gray matter (GM) and white matter (WM) tissues using each model. Comparative analyses were performed to assess the range of predicted conductivities and the consistency between bilateral tissue conductivities for each method.

Results

Significant variability in conductivity estimates was observed across the three models. Each method predicted distinct conductivity values for GM and WM tissues, with notable differences in the range of conductivities observed for specific tissue examples. Despite the variability, many WM and GM tissues exhibited symmetric bilateral conductivities within each microstructure model. SMT yielded conductivity estimates closer to values reported in experimental studies, while none of the methods aligned with spectroscopic models of tissue conductivity.

Discussion and conclusion

Our findings highlight substantial discrepancies in tissue conductivity estimates generated by different diffusion models, underscoring the challenge of selecting an appropriate model for low-frequency electrical conductivity imaging. SMT demonstrated better alignment with experimental results. However other microstructure models may produce better tissue discrimination.

Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI

Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute-MNI space are publicly available. RELEVANCE STATEMENT: Our method to automatically characterise cerebral microbleeds (size and location) showed a mean absolute error of 2.5 mm for size estimation and an over 90% accuracy for rating of infratentorial, deep and lobar regions. This is a promising approach to automatically provide clinically relevant cerebral microbleeds metrics. KEY POINTS: We present a method to automatically characterise cerebral microbleeds according to size and location. The method achieved a mean absolute error of 2.5 mm for size estimation. Automated rating for infratentorial, deep, and lobar regions achieved an over 90% overall accuracy. We made the code and atlas of Microbleed Anatomical Rating Scale regions publicly available.

Co-methylation networks associated with cognition and structural brain development during adolescence

Introduction

Typical adolescent neurodevelopment is marked by decreases in grey matter (GM) volume, increases in myelination, measured by fractional anisotropy (FA), and improvement in cognitive performance.

Methods

To understand how epigenetic changes, methylation (DNAm) in particular, may be involved during this phase of development, we studied cognitive assessments, DNAm from saliva, and neuroimaging data from a longitudinal cohort of normally developing adolescents, aged nine to fourteen. We extracted networks of methylation with patterns of correlated change using a weighted gene correlation network analysis (WCGNA). Modules from these analyses, consisting of co-methylation networks, were then used in multivariate analyses with GM, FA, and cognitive measures to assess the nature of their relationships with cognitive improvement and brain development in adolescence.

Results

This longitudinal exploration of co-methylated networks revealed an increase in correlated epigenetic changes as subjects progressed into adolescence. Co-methylation networks enriched for pathways involved in neuronal systems, potassium channels, neurexins and neuroligins were both conserved across time as well as associated with maturation patterns in GM, FA, and cognition.

Discussion

Our research shows that correlated changes in the DNAm of genes in neuronal processes involved in adolescent brain development that were both conserved across time and related to typical cognitive and brain maturation, revealing possible epigenetic mechanisms driving this stage of development.

Focal leptomeningeal vascular anomalies on brain MRI: A mimic of leptomeningeal metastatic disease

Background

The diagnosis of leptomeningeal metastatic disease has major prognostic and therapeutic implications. We report 13 patients with a radiologically distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics leptomeningeal metastatic disease.

Methods

These patients were assessed at University Health Network (Toronto, Canada) between January 2001 and December 2023.

Results

Median age was 68 years (range, 55-78 years) and 10 patients were women. All patients had brain magnetic resonance imaging (MRI) including contrast-enhanced T2-weighted fluid attenuation inversion recovery (FLAIR) and T1-weighted spin echo sequences. MRI in all 13 patients showed a focal enhancing lesion located along the leptomeningeal surface of the brain. The MRI exams were reported as possible or likely leptomeningeal metastatic disease for the majority (9/13) of patients. Each lesion was curvilinear rather than sheet-like, and some lesions consisted of multiple connected/branching curvilinear structures with the appearance of abnormal vessels. The lesions were distinct from normal blood vessels. Some lesions had a visible connection with a nearby cortical vein. Follow-up contrast-enhanced brain MRI for 8/13 (62%) patients at a median of 3.9 years (IQR 2.4-6.6 years) showed all lesions were unchanged over time. Another 2/13 (15%) patients had clinical and CT brain follow-up after one year with no evidence of metastatic disease.

Conclusions

We describe a distinct kind of focal, enhancing leptomeningeal lesion on brain MRI that mimics metastatic disease. These lesions are likely a type of low-flow vascular anomaly. Their curvilinear/branching shape and intense enhancement particularly on T2-weighted FLAIR images distinguish these lesions from tumors.

Segmental MRI pituitary and hypothalamus volumes post Fontan: An analysis of the Australian and New Zealand Fontan registry

Objective

Short stature, central hypothyroidism and infertility are common in those with a Fontan circulation. Given that the Fontan circulation often results in hepatic portal venous congestion, we hypothesize that the hypothalamic-pituitary portal circulation is also affected, contributing to subsequent hypothalamic-pituitary axis dysfunction.

Methods

MRI data from the Australian and New Zealand Fontan Registry (86 cases) was compared to 86 age- and sex-matched normal published controls. Total pituitary volumes (both anterior and posterior glands) were measured using a manual tracing segmentation method, and hypothalamic (and subunit) volumes using an automated segmentation tool. Measured gland volume was normalized to total brain volumes. A generalized linear model was used for statistical analysis.

Results

Normalized total pituitary volumes (nTPV) were increased in Fontan patients compared to controls (p < 0.0001), due to an increase in anterior pituitary volumes (nAPV) (p < 0.0001), with no difference in normalized posterior pituitary volumes (p = 0.7). Furthermore, normalized anterior and tubular hypothalamic subunit groups) were increased in Fontan patients compared to the controls (p < 0.01 and p < 0.0001, respectively).The time between Fontan and MRI was positively related to nTPV, nAPV and bilateral hypothalamic volumes. nTPV increased with age, and the increase in nAPV was greater in Fontan patients.

Conclusions

Segmental MRI Pituitary and Hypothalamus volumes post Fontan are increased and are related to the time since Fontan procedure. These findings are consistent with venous congestion of the anterior hypothalamic-pituitary portal venous system and may explain the high frequency of endocrine dysfunction in this patient group.

MRI-based whole-brain elastography and volumetric measurements to predict brain age

Brain age, as a correlate of an individual's chronological age obtained from structural and functional neuroimaging data, enables assessing developmental or neurodegenerative pathology relative to the overall population. Accurately inferring brain age from brain magnetic resonance imaging (MRI) data requires imaging methods sensitive to tissue health and sophisticated statistical models to identify the underlying age-related brain changes. Magnetic resonance elastography (MRE) is a specialized MRI technique which has emerged as a reliable, non-invasive method to measure the brain's mechanical properties, such as the viscoelastic shear stiffness and damping ratio. These mechanical properties have been shown to change across the life span, reflect neurodegenerative diseases, and are associated with individual differences in cognitive function. Here, we aim to develop a machine learning framework to accurately predict a healthy individual's chronological age from maps of brain mechanical properties. This framework can later be applied to understand neurostructural deviations from normal in individuals with neurodevelopmental or neurodegenerative conditions. Using 3D convolutional networks as deep learning models and more traditional statistical models, we relate chronological age as a function of multiple modalities of whole-brain measurements: stiffness, damping ratio, and volume. Evaluations on held-out subjects show that combining stiffness and volume in a multimodal approach achieves the most accurate predictions. Interpretation of the different models highlights important regions that are distinct between the modalities. The results demonstrate the complementary value of MRE measurements in brain age models, which, in future studies, could improve model sensitivity to brain integrity differences in individuals with neuropathology.

"Quality over quantity:" smaller, targeted lesions optimize quality of life outcomes after MR-guided focused ultrasound thalamotomy for essential tremor

Introduction

MRI-guided focused ultrasound (MRgFUS) thalamotomy of the nucleus ventralis intermedius (VIM) has emerged as a powerful and safe treatment modality for refractory essential tremor. While the efficacy of this technique has been extensively described, much remains unclear about how to optimize MRgFUS for patient quality of life (QoL), which may depend as much on a patient's adverse effect profile as on the magnitude of tremor suppression. Diffusion tensor imaging (DTI) has been used to help guide targeting strategies but can pose certain challenges for scalability.

Methods

In this study, we propose the use of a simplified patient-reported change in QoL assessment to create an unbiased representation of a patient's perception of overall benefit. Further, we propose a large-sample-size, high-resolution, 7 T DTI database from the Human Connectome Project to create a normative tractographic atlas (NTA) with representations of ventral intermediate nucleus subregions most likely to be structurally connected to the motor cortex. The NTA network-based hotspots are then nonlinearly fitted to each patient's T1-weighted MRI.

Results and discussion

We found that smaller lesion size and higher extent to which the lesion is within the NTA hotspot predicted patients' change in QoL at last follow-up. Though long-term change in clinical rating scale for tremor (CRST) impacted QoL, neither intraoperative tremor suppression nor the patient's long-term perception of tremor suppression correlated with QoL. We provide an intraoperative threshold for accumulated dose volume (<0.06 cc), which along with the network-based hotspot in the NTA, may facilitate an easily scalable approach to help limit treatment to small, safe yet effective lesions that optimize change in QoL after MRgFUS.

Changes in brain connectivity and neurovascular dynamics during dexmedetomidine-induced loss of consciousness

Understanding the neurophysiological changes that occur during loss and recovery of consciousness is a fundamental aim in neuroscience and has marked clinical relevance. Here, we utilize multimodal magnetic resonance neuroimaging to investigate changes in regional network connectivity and neurovascular dynamics as the brain transitions from wakefulness to dexmedetomidine-induced unconsciousness, and finally into early-stage recovery of consciousness. We observed widespread decreases in functional connectivity strength across the whole brain, and targeted increases in structure-function coupling (SFC) across select networks-especially the cerebellum-as individuals transitioned from wakefulness to hypnosis. We also observed robust decreases in cerebral blood flow (CBF) across the whole brain-especially within the brainstem, thalamus, and cerebellum. Moreover, hypnosis was characterized by significant increases in the amplitude of low-frequency fluctuations (ALFF) of the resting-state blood oxygen level-dependent signal, localized within visual and somatomotor regions. Critically, when transitioning from hypnosis to the early stages of recovery, functional connectivity strength and SFC-but not CBF-started reverting towards their awake levels, even before behavioral arousal. By further testing for a relationship between connectivity and neurovascular alterations, we observed that during wakefulness, brain regions with higher ALFF displayed lower functional connectivity with the rest of the brain. During hypnosis, brain regions with higher ALFF displayed weaker coupling between structural and functional connectivity. Correspondingly, brain regions with stronger functional connectivity strength during wakefulness showed greater reductions in CBF with the onset of hypnosis. Earlier recovery of consciousness was associated with higher baseline (awake) levels of functional connectivity strength, CBF, and ALFF, as well as female sex. Across our findings, we also highlight the role of the cerebellum as a recurrent marker of connectivity and neurovascular changes between states of consciousness. Collectively, these results demonstrate that induction of, and emergence from dexmedetomidine-induced unconsciousness are characterized by widespread changes in connectivity and neurovascular dynamics.

Impact of Harmonization on MRI Radiomics Feature Variability Across Preprocessing Methods for Parkinson's Disease Motor Subtype Classification

This study aimed to assess the reproducibility of MRI-derived radiomic features across multiple preprocessing methods for classifying Parkinson's disease (PD) motor subtypes and to evaluate the impact of ComBat harmonization on feature stability and machine learning performance. T1-weighted MRI scans from 140 PD patients (70 tremor-dominant and 70 postural instability gait difficulty) and 70 healthy controls were obtained from the Parkinson's Progression Markers Initiative (PPMI) database, acquired using different scanner models. Radiomic features were extracted from 16 brain regions using various preprocessing pipelines. ComBat harmonization was applied using a combined batch variable incorporating both scanner models and preprocessing methods. Intraclass correlation coefficients (ICC) and Kruskal-Wallis tests assessed feature reproducibility before and after harmonization. Feature selection was performed using Linear Support Vector Classifier with L1 regularization. Support vector machine classifiers were used for PD subtype classification. ComBat harmonization significantly improved feature reproducibility across all feature groups. The percentage of features showing excellent robustness (ICC ≥ 0.90) increased from 40.2 to 56.3% after harmonization. First-order statistic features showed the highest robustness, with 71.11% demonstrating excellent ICC after harmonization. The proportion of features significantly affected by preprocessing methods was reduced following harmonization. Classification accuracy improved dramatically, from a range of 34-75% before harmonization to 89-96% after harmonization across all preprocessing methods. AUC values similarly increased from 0.28-0.87 to 0.95-0.99 after harmonization. ComBat harmonization significantly enhanced the reproducibility of radiomic features across preprocessing methods and improved PD motor subtype classification performance. This study highlights the importance of harmonization in radiomics research for PD and suggests potential clinical applications in personalized treatment planning.

Proximity within adolescent peer networks predicts neural similarity during affective experience

Individuals befriend others who are similar to them. One important source of similarity in relationships is similarity in felt emotion. In the present study, we used novel methods to assess whether greater similarity in the multivoxel brain representation of affective stimuli was associated with adolescents' proximity within real-world school-based social networks. We examined dyad-level neural similarity within a set of brain regions associated with the representation of affect including the ventromedial prefrontal cortex (vmPFC), amygdala, insula, and temporal pole. Greater proximity was associated with greater vmPFC neural similarity during pleasant and neutral experiences. Moreover, we used unsupervised clustering on social networks to identify groups of friends and observed that individuals from the same (versus different) friend groups were more likely to have greater vmPFC neural similarity during pleasant and negative experiences. These findings suggest that similarity in the multivoxel brain representation of affect may play an important role in adolescent friendships.

Altered Functional Connectivity of Unimodal Sensory and Multisensory Integration Networks Is Related to Symptom Severity in Autism Spectrum Disorder

BACKGROUND

Atypical sensory processing is a prevalent feature of autism spectrum disorder (ASD) and constitutes a core diagnostic criterion in DSM-5. However, the neurocognitive underpinnings of atypical unimodal and multimodal sensory processing and their relationships with autism symptoms remain unclear.

METHODS

In this study, we examined intrinsic functional connectivity (FC) patterns among 5 unimodal sensory and multisensory integration (MSI) networks in ASD using a large multisite dataset (N = 646) and investigated the relationships between altered FC, atypical sensory processing, social communicative deficits, and overall autism symptoms using correlation and mediation analyses.

RESULTS

Compared with typically developing control participants, participants in the ASD group demonstrated increased FC of the olfactory network, decreased FC within the MSI network, and decreased FC of the MSI-unimodal sensory networks. Furthermore, altered FC was positively associated with autism symptom severity, and such associations were completely mediated by atypical sensory processing and social communicative deficits.

CONCLUSIONS

ASD-specific olfactory overconnectivity and MSI-unimodal sensory underconnectivity lend support to the intense world theory and weak central coherence theory, suggesting olfactory hypersensitivity at the expense of MSI as a potential neural mechanism underlying atypical sensory processing in ASD. These atypical FC patterns suggest potential targets for psychological and neuromodulatory interventions.

Increased functional connectivity between brain regions involved in social cognition, emotion and affective-value in psychedelic states induced by N,N-Dimethyltryptamine (DMT)

The modulation of social cognition is suggested as a possible mechanism contributing to the potential clinical efficacy of psychedelics in disorders involving socio-emotional and reward processing deficits. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) can be used to detect changes in brain connectivity during psychedelic-induced states. Thus, this pharmacoimaging study investigates the effects of N,N-Dimethyltryptamine (DMT) on functional connectivity in brain areas relevant to social cognition, using a within-subject design in eleven healthy experienced users. The study included both an active and a control condition, conducted at different time points. The active condition involved DMT inhalation, while the control condition did not. Seed-based connectivity was measured for the two core regions involved in theory of mind and emotional processing, respectively, the posterior supramarginal gyrus and the amygdala. DMT increased supramarginal gyrus connectivity with the precuneus, posterior cingulate gyrus, amygdala, and orbitofrontal cortex. Additionally, increased connectivity emerged between the amygdala and orbitofrontal cortex. These results demonstrate that DMT modulates brain connectivity in socio-emotional and affective-value circuits, advancing our understanding of the neural mechanisms underlying the psychedelic experience and its potential therapeutic action.

Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression

BACKGROUND/OBJECTIVES

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation intervention that shows promise as a potential treatment for depression. However, the clinical efficacy of tDCS varies, possibly due to individual differences in head anatomy affecting tDCS dosage. While functional changes in brain activity are more commonly reported in major depressive disorder (MDD), some studies suggest that subtle macroscopic structural differences, such as cortical thickness or brain volume reductions, may occur in MDD and could influence tDCS electric field (E-field) distributions. Therefore, accounting for individual anatomical differences may provide a pathway to optimize functional gains in MDD by formulating personalized tDCS dosage.

METHODS

To address the dosing variability of tDCS, we examined a subsample of sixteen active-tDCS participants' data from the larger ELECT clinical trial (NCT01894815). With this dataset, individualized neuroimaging-derived computational models of tDCS current were generated for (1) classifying treatment response, (2) elucidating essential stimulation features associated with treatment response, and (3) computing a personalized dose of tDCS to maximize the likelihood of treatment response in MDD.

RESULTS

In the ELECT trial, tDCS was superior to placebo (3.2 points [95% CI, 0.7 to 5.5; P = 0.01]). Our algorithm achieved over 90% overall accuracy in classifying treatment responders from the active-tDCS group (AUC = 0.90, F1 = 0.92, MCC = 0.79). Computed precision doses also achieved an average response likelihood of 99.981% and decreased dosing variability by 91.9%.

CONCLUSION

These findings support our previously developed precision-dosing method for a new application in psychiatry by optimizing the statistical likelihood of tDCS treatment response in MDD.

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

Tractography of sensorimotor pathways in dyskinetic cerebral palsy: Association with motor function

OBJECTIVES

Neuroimaging studies of dyskinetic cerebral palsy (CP) are scarce and the neuropathological underpinnings are not fully understood. We delineated the corticospinal tract (CST) and cortico-striatal-thalamocortical (CSTC) pathways with probabilistic tractography to assess their (1) integrity and (2) association with motor functioning in people with dyskinetic CP.

METHODS

Diffusion weighted magnetic resonance images were obtained for 33 individuals with dyskinetic CP and 33 controls. Fractional anisotropy (FA) and mean diffusivity (MD) for the CST and the CSTC pathways were compared between groups. Correlation analyses were performed between tensor metric values and motor function scores of participants with dyskinetic CP as assessed by the Gross Motor Function Classification System (GMFCS), the Bimanual Fine Motor Function (BFMF), and the Manual Ability Classification System (MACS).

RESULTS

White matter integrity in both the CST and the CSTC pathways was reduced in people with dyskinetic CP. The GMFCS, MACS and, less commonly, the BFMF were associated with FA and, particularly, MD in most portions of these pathways.

INTERPRETATION

The present study advances our understanding of the involvement of white matter microstructure in sensorimotor pathways and its relationship with motor impairment in people with dyskinetic CP. Our results are consistent with well-described relationships between upper limb function and white matter integrity in the CST and CSTC pathways in other forms of CP. This knowledge may ultimately help prognosis and therapeutic programmes.

Motor versus Psychomotor? Deciphering the Neural Source of Psychomotor Retardation in Depression

Major depressive disorder (MDD) is characterized by psychomotor retardation whose underlying neural source remains unclear. Psychomotor retardation may either be related to a motor source like the motor cortex or, alternatively, to a psychomotor source with neural changes outside motor regions, like input regions such as visual cortex. These two alternative hypotheses in main (n = 41) and replication (n = 18) MDD samples using 7 Tesla MRI are investigated. Analyzing both global and local connectivity in primary motor cortex (BA4), motor network and middle temporal visual cortex complex (MT+), the main findings in MDD are: 1) Reduced local and global synchronization and increased local-to-global output in motor regions, which do not correlate with psychomotor retardation, though. 2) Reduced local-to-local BA4 - MT+ functional connectivity (FC) which correlates with psychomotor retardation. 3) Reduced global synchronization and increased local-to-global output in MT+ which relate to psychomotor retardation. 4) Reduced variability in the psychophysical measures of MT+ based motion perception which relates to psychomotor retardation. Together, it is shown that visual cortex MT+ and its relation to motor cortex play a key role in mediating psychomotor retardation. This supports psychomotor over motor hypothesis about the neural source of psychomotor retardation in MDD.

Exploring the Utility of a Functional Magnetic Resonance Imaging (fMRI) Cannabis Cue-Reactivity Paradigm in Treatment Seeking Adults with Cannabis Use Disorder

BACKGROUND

Functional magnetic resonance imaging (fMRI) studies examining cue-reactivity in cannabis use disorder (CUD) have either had small sample sizes or involved non-treatment-seeking participants. As a secondary analysis, we administered an fMRI cue-reactivity task to CUD participants entering two separate clinical trials (varenicline or repetitive Transcranial Magnetic Stimulation-rTMS) to determine the task activation patterns for treatment-seeking participants with CUD. We aimed to determine the activation patterns for the total sample and determined behavioral correlates. We additionally compared studies to determine if patterns were consistent.

METHODS

Treatment-seeking participants with moderate or severe CUD had behavioral craving measured at baseline via the short form of the Marijuana Craving Questionnaire (MCQ-SF) and completed a visual cannabis cue-reactivity task during fMRI (measuring the Blood-Oxygen-Level-Dependent-BOLD response) following 24-hours of cannabis-abstinence.

RESULTS

Sixty-five participants were included (37-varenicline, 28-rTMS; 32% female; mean-age 30.4±9.9SD). When contrasting cannabis-images vs. matched-neutral-images, participants showed greater BOLD response in bilateral ventromedial-prefrontal, dorsolateral-prefrontal, anterior cingulate, and visual cortices, as well as the striatum. There was stronger task-based functional-connectivity (tbFC) between the medial prefrontal cortex and both the amygdala and the visual cortex. Craving negatively correlated with BOLD response in the left ventral striatum (R2=-0.32; p=0.01) in the full sample. There were no significant differences in either activation or tbFC between studies.

DISCUSSION

Among two separate treatment-seeking groups with CUD, there was increased cannabis cue-reactivity and tbFC in regions related to executive function and reward processing. Cannabis-craving was negatively associated with cue-reactivity in the left ventral striatum.

Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson's disease motor subtypes in early-stages

This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55-0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Atypical lateralization of visuospatial attention can be associated with better or worse performance on line bisection

The causal and statistical hypotheses diverge in determining whether the lateralization of language function in one cerebral hemisphere entails the lateralization of visuospatial function in the opposite hemisphere. Additionally, it remains unclear if the atypical segregation of these functions could influence cognitive performance. This study addresses these questions by examining the hemispheric lateralization of visuospatial attention during a line bisection judgement (landmark) task in three groups of healthy non-right-handed individuals with different language production segregations: left (typical), ambilateral (atypical), and right (atypical). Consistent with the causal hypothesis, results indicate that the groups with left and right language lateralization primarily utilize the opposite hemisphere for visuospatial attention. The ambilateral group, however, displays a pattern compatible with an independent segregation, supporting the statistical hypothesis. Behavioral analyses reveal that atypical lateralization of visuospatial attention (non-right) can lead to either better or worse performance during the landmark task, depending on the specific pattern. Bilateral organization is associated with reduced overall accuracy, whereas the left segregation results in improved performance during the most challenging trials. These findings suggest the existence of diverse pathways to lateralization, akin to either the causal or statistical hypothesis, which can result in cognitive advantages or disadvantages.

Where alcohol use disorder meets interoception: A meta-analytic view on structural and functional neuroimaging data

Alcohol use disorder (AUD) has been associated with changes in the processing of internal body signals, known as interoception. Changes in brain structure, particularly in the insula, are thought to underlie impaired interoception. As studies specifically investigating this association are largely lacking, this analysis takes an approach that compares meta-analytic results on interoception with recently published meta-analytic results on gray matter reduction in AUD. A systematic literature search identified 25 eligible interoception studies. Activation likelihood estimation (ALE) was used to test for spatial convergence of study results. Overlap between interoception and AUD clusters was tested using conjunction analysis. Meta-analytic connectivity modeling (MACM) and resting-state functional connectivity were used to identify the functional network of interoception and to test where this network overlapped with AUD meta-analytic clusters. The results were characterized using behavioral domain analysis. The interoception ALE identified a cluster in the left middle insula. There was no overlap with clusters of reduced gray matter in AUD. MACM analysis of the interoception cluster revealed a large network located in the insulae, thalami, basal nuclei, cingulate and medial frontal cortices, and pre- and postcentral gyri. Resting state analysis confirmed this result, showing the strongest connections to nodes of the salience- and somatomotor network. Five of the eight clusters that showed a structural reduction in AUD were located within these networks. The behavioral profiles of these clusters were suggestive of higher-level processes such as salience control, somatomotor functions, and skin sensations. The results suggest an altered salience mapping of interoceptive signals in AUD, consistent with current models. Connections to the somatomotor network may be related to action control and integration of skin sensations. Mindfulness-based interventions, pleasurable touch, and (deep) transcranial magnetic stimulation may be targeted interventions that reduce interoceptive deficits in AUD and thus contribute to drug use reduction and relapse prevention.

Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults

Introduction: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. Methods: To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). Results: We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. Conclusion: These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Language and the Cerebellum: Structural Connectivity to the Eloquent Brain

Neurobiological models of receptive language have focused on the left-hemisphere perisylvian cortex with the assumption that the cerebellum supports peri-linguistic cognitive processes such as verbal working memory. The goal of this study was to identify language-sensitive regions of the cerebellum then map the structural connectivity profile of these regions. Functional imaging data and diffusion-weighted imaging data from the Human Connectome Project (HCP) were analyzed. We found that (a) working memory, motor activity, and language comprehension activated partially overlapping but mostly unique subregions of the cerebellum; (b) the linguistic portion of the cerebello-thalamo-cortical circuit was more extensive than the linguistic portion of the cortico-ponto-cerebellar tract; (c) there was a frontal-lobe bias in the connectivity from the cerebellum to the cerebrum; (d) there was some degree of specificity; and (e) for some cerebellar tracts, individual differences in picture identification ability covaried with fractional anisotropy metrics. These findings yield insights into the structural connectivity of the cerebellum as relates to the uniquely human process of language comprehension.

Resting-State Functional Connectivity Profile of Insular Subregions

The insula is often considered the fifth lobe of the brain and is increasingly recognized as one of the most connected regions in the brain, with widespread connections to cortical and subcortical structures. As a follow-up to our previous tractography work, we investigated the resting-state functional connectivity (rsFC) profiles of insular subregions and assessed their concordance with structural connectivity. We used the CONN toolbox to analyze the rsFC of the same 19 insular regions of interest (ROIs) we used in our prior tractography work and regrouped them into six subregions based on their connectivity pattern similarity. Our analysis of 50 healthy participants confirms the known broad connectivity of the insula and shows novel and specific whole-brain and intra-connectivity patterns of insular subregions. By examining such subregions, our findings provide a more detailed pattern of connectivity than prior studies that may prove useful for comparison between patients.

Neurofilaments and progranulin are related to atrophy in frontotemporal lobar degeneration - A transdiagnostic study cross-validating atrophy and fluid biomarkers

INTRODUCTION

Frontotemporal lobar degeneration (FTLD) encompasses behavioral variant frontotemporal dementia (bvFTD), progressive supranuclear palsy, corticobasal syndrome/degeneration, and primary progressive aphasias (PPAs). We cross-validated fluid biomarkers and neuroimaging.

METHODS

Seven fluid biomarkers from cerebrospinal fluid and serum were related to atrophy in 428 participants including these FTLD subtypes, logopenic variant PPA (lvPPA), Alzheimer's disease (AD), and healthy subjects. Atrophy was assessed by structural magnetic resonance imaging and atlas-based volumetry.

RESULTS

FTLD subtypes, lvPPA, and AD showed specific profiles for neurofilament light chain, phosphorylated heavy chain, tau, phospho-tau, amyloid beta1-42 from serum/cerebrospinal fluid, and brain atrophy. Neurofilaments related to regional atrophy in bvFTD, whereas progranulin was associated with atrophy in semantic variant PPA. Ubiquitin showed no effects.

DISCUSSION

Results specify biomarker and atrophy patterns in FTLD and AD supporting differential diagnosis. They identify neurofilaments and progranulin in interaction with structural imaging as promising candidates for monitoring disease progression and therapy.

HIGHLIGHTS

Study cross-validated neuroimaging and fluid biomarkers in dementia. Five kinds of frontotemporal lobar degeneration and two variants of Alzheimer's disease. Study identifies disease-specific fluid biomarker and atrophy profiles. Fluid biomarkers and atrophy interact in a disease-specific way. Neurofilaments and progranulin are proposed as biomarkers for diagnosis and therapy.

Test-retest reliability of resting-state cerebral blood flow quantification using pulsed Arterial Spin Labeling (PASL) over 3 weeks vs 8 weeks in healthy controls

Arterial Spin Labeling is a valuable functional imaging tool for both clinical and research purposes. However, little is known about the test-retest reliability of cerebral blood flow measurements over longer periods. In this study, we investigated the reliability of pulsed Arterial Spin Labeling in assessing cerebral blood flow over a 3 (n = 28) vs 8 (n = 19) weeks interscan interval in 47 healthy participants. As a measure of cerebral blood flow reliability, we calculated voxel-wise, whole-brain, and regions of interest intraclass correlation coefficients. The whole-brain mean resting-state cerebral blood flow showed good to excellent reliability over time for both periods (intraclass correlation coefficients = 0.85 for the 3-week delay, intraclass correlation coefficients = 0.53 for the 8-week delay). However, the voxel-wise and regions of interest intraclass correlation coefficients fluctuated at 8-week compared to the 3-week interval, especially within cortical areas. These results confirmed previous findings that Arterial Spin Labeling could be used as a reliable method to assess brain perfusion. However, as the reliability seemed to decrease over time, caution is warranted when performing correlations with other variables, especially in clinical populations.

Resting state neurophysiology of agonist-antagonist myoneural interface in persons with transtibial amputation

The agonist-antagonist myoneural interface (AMI) is an amputation surgery that preserves sensorimotor signaling mechanisms of the central-peripheral nervous systems. Our first neuroimaging study investigating AMI subjects conducted by Srinivasan et al. (2020) focused on task-based neural signatures, and showed evidence of proprioceptive feedback to the central nervous system. The study of resting state neural activity helps non-invasively characterize the neural patterns that prime task response. In this study on resting state functional magnetic resonance imaging in AMI subjects, we compared functional connectivity in patients with transtibial AMI (n = 12) and traditional (n = 7) amputations (TA). To test our hypothesis that we would find significant neurophysiological differences between AMI and TA subjects, we performed a whole-brain exploratory analysis to identify a seed region; namely, we conducted ANOVA, followed by t-test statistics to locate a seed in the salience network. Then, we implemented a seed-based connectivity analysis to gather cluster-level inferences contrasting our subject groups. We show evidence supporting our hypothesis that the AMI surgery induces functional network reorganization resulting in a neural configuration that significantly differs from the neural configuration after TA surgery. AMI subjects show significantly less coupling with regions functionally dedicated to selecting where to focus attention when it comes to salient stimuli. Our findings provide researchers and clinicians with a critical mechanistic understanding of the effect of AMI amputation on brain networks at rest, which has promising implications for improved neurorehabilitation and prosthetic control.

Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients

Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.

Pathological claustrum activity drives aberrant cognitive network processing in human chronic pain

Aberrant cognitive network activity and cognitive deficits are established features of chronic pain. However, the nature of cognitive network alterations associated with chronic pain and their underlying mechanisms require elucidation. Here, we report that the claustrum, a subcortical nucleus implicated in cognitive network modulation, is activated by acute painful stimulation and pain-predictive cues in healthy participants. Moreover, we discover pathological activity of the claustrum and a region near the posterior inferior frontal sulcus of the right dorsolateral prefrontal cortex (piDLPFC) in migraine patients during acute pain and cognitive task performance. Dynamic causal modeling suggests a directional influence of the claustrum on activity in this piDLPFC region, and diffusion weighted imaging verifies their structural connectivity. These findings advance understanding of claustrum function during acute pain and provide evidence of a possible circuit mechanism driving cognitive impairments in chronic pain.

Relationship between functional connectivity and weight-gain risk of antipsychotics in schizophrenia

BACKGROUND

The mechanisms by which antipsychotic medications (APs) contribute to obesity in schizophrenia are not well understood. Because AP effects on functional brain connectivity may contribute to weight effects, the current study investigated how AP-associated weight-gain risk relates to functional connectivity in schizophrenia.

METHODS

Fifty-five individuals with schizophrenia (final N = 54) were divided into groups based on previously reported AP weight-gain risk (no APs/low risk [N = 19]; moderate risk [N = 17]; high risk [N = 18]). Resting-state functional magnetic resonance imaging (fMRI) was completed after an overnight fast ("fasted") and post-meal ("fed"). Correlations between AP weight-gain risk and functional connectivity were assessed at the whole-brain level and in reward- and eating-related brain regions (anterior insula, caudate, nucleus accumbens).

RESULTS

When fasted, greater AP weight-gain risk was associated with increased connectivity between thalamus and sensorimotor cortex (pFDR = 0.021). When fed, greater AP weight-gain risk was associated with increased connectivity between left caudate and left precentral/postcentral gyri (pFDR = 0.048) and between right caudate and multiple regions, including the left precentral/postcentral gyri (pFDR = 0.001), intracalcarine/precuneal/cuneal cortices (pFDR < 0.001), and fusiform gyrus (pFDR = 0.008). When fed, greater AP weight-gain risk was also associated with decreased connectivity between right anterior insula and ventromedial prefrontal cortex (pFDR = 0.002).

CONCLUSIONS

APs with higher weight-gain risk were associated with greater connectivity between reward-related regions and sensorimotor regions when fasted, perhaps relating to motor anticipation for consumption. Higher weight-gain risk APs were also associated with increased connectivity between reward, salience, and visual regions when fed, potentially reflecting greater desire for consumption following satiety.

Hypothalamic Subunit Volumes in Schizophrenia and Bipolar Spectrum Disorders

BACKGROUND

The hypothalamus is central to many hormonal and autonomous nervous system pathways. Emerging evidence indicates that these pathways may be disrupted in schizophrenia and bipolar disorder. Yet, few studies have examined the volumes of hypothalamic subunits in these patient groups. We compared hypothalamic subunit volumes in individuals with psychotic disorders to healthy controls.

STUDY DESIGN

We included 344 patients with schizophrenia spectrum disorders (SCZ), 340 patients with bipolar disorders (BPD), and 684 age- and-sex-matched healthy controls (CTR). Total hypothalamus and five hypothalamic subunit volumes were extracted from T1-weighted magnetic resonance imaging (MRI) using an automated Bayesian segmentation method. Regression models, corrected for age, age2, sex, and segmentation-based intracranial volume (sbTIV), were used to examine diagnostic group differences, interactions with sex, and associations with clinical symptoms, antipsychotic medication, antidepressants and mood stabilizers.

STUDY RESULTS

SCZ had larger volumes in the left inferior tubular subunit and smaller right anterior-inferior, right anterior-superior, and right posterior hypothalamic subunits compared to CTR. BPD did not differ significantly from CTR for any hypothalamic subunit volume, however, there was a significant sex-by-diagnosis interaction. Analyses stratified by sex showed smaller right hypothalamus and right posterior subunit volumes in male patients, but not female patients, relative to same-sex controls. There was a significant association between BPD currently taking antipsychotic medication and the left inferior tubular subunits volumes.

CONCLUSIONS

Our results show regional-specific alterations in hypothalamus subunit volumes in individuals with SCZ, with relevance to HPA-axis dysregulation, circadian rhythm disruption, and cognition impairment.

Altered Functional Connectivity during Mild Transient Respiratory Impairment Induced by a Resistive Load

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without altering transcutaneous oxygen saturation. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptual area (postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores. These findings may provide new insights into the visual networks that contribute to mild respiratory impairments.

Low Functional network integrity in cognitively unimpaired and MCI subjects with depressive symptoms: results from a multi-center fMRI study

Evidence suggests that depressive symptomatology is a consequence of network dysfunction rather than lesion pathology. We studied whole-brain functional connectivity using a Minimum Spanning Tree as a graph-theoretical approach. Furthermore, we examined functional connectivity in the Default Mode Network, the Frontolimbic Network (FLN), the Salience Network, and the Cognitive Control Network. All 183 elderly subjects underwent a comprehensive neuropsychological evaluation and a 3 Tesla brain MRI scan. To assess the potential presence of depressive symptoms, the 13-item version of the Beck Depression Inventory (BDI) or the Geriatric Depression Scale (GDS) was utilized. Participants were assigned into three groups based on their cognitive status: amnestic mild cognitive impairment (MCI), non-amnestic MCI, and healthy controls. Regarding affective symptoms, subjects were categorized into depressed and non-depressed groups. An increased mean eccentricity and network diameter were found in patients with depressive symptoms relative to non-depressed ones, and both measures showed correlations with depressive symptom severity. In patients with depressive symptoms, a functional hypoconnectivity was detected between the Anterior Cingulate Cortex (ACC) and the right amygdala in the FLN, which impairment correlated with depressive symptom severity. While no structural difference was found in subjects with depressive symptoms, the volume of the hippocampus and the thickness of the precuneus and the entorhinal cortex were decreased in subjects with MCI, especially in amnestic MCI. The increase in eccentricity and diameter indicates a more path-like functional network configuration that may lead to an impaired functional integration in depression, a possible cause of depressive symptomatology in the elderly.

Predictability of intelligence and age from structural connectomes

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

The impact of past childhood adversity and recent life events on neural responses during fear conditioning

BACKGROUND AND PURPOSE

Many studies have shown that exposure to life events can have a negative impact on mental health. Life events like the death of a spouse or the birth of a child pose a challenge and require temporal or permanent adjustments. Meta-analyses on brain stress responses found bilateral anterior insula activation in response to acute stress. Fear conditioning is assumed a crucial mechanism for the development of anxiety disorders associated with increased activation in the bilateral amygdala. Empirical evidence is lacking regarding the relationship of exposure to recent life events and past childhood adversity with neural processing during fear conditioning.

METHODS

In the present study, we analyzed data from 103 young, healthy participants. Multiple linear regressions were performed on functional magnetic resonance imaging activation during fear conditioning with the Life Events Scale for Students and the Childhood Trauma questionnaire included as covariates in two separate models.

RESULTS

We found a positive relationship between the number of life events in the last year and left amygdala activation to the conditioned stimulus. A second finding was a positive relationship between childhood adversity and right anterior insula response to the unconditioned stimulus.

CONCLUSIONS

Many studies have shown increased amygdala activity after stressful life events. In addition, the anterior insula is activated during acute stress. The present study points to stressor-induced increased salience processing during fear conditioning. We suggest that this could be a potential mechanism for resilience versus mental illness.

Mapping Brain Structure Variability in Chronic Pain: The Role of Widespreadness and Pain Type and Its Mediating Relationship With Suicide Attempt

BACKGROUND

Chronic pain affects nearly 20% of the U.S.

POPULATION

It is a leading cause of disability globally and is associated with a heightened risk for suicide. The role of the central nervous system in the perception and maintenance of chronic pain has recently been accepted, but specific brain circuitries involved have yet to be mapped across pain types in a large-scale study.

METHODS

We used data from the UK Biobank (N = 21,968) to investigate brain structural alterations in individuals reporting chronic pain compared with pain-free control participants and their mediating effect on history of suicide attempt.

RESULTS

Chronic pain and, more notably, chronic multisite pain was associated with, on average, lower surface area throughout the cortex after adjusting for demographic, clinical, and neuropsychiatric confounds. Only participants with abdominal pain showed lower subcortical volumes, including the amygdala and brainstem, and lower cerebellum volumes. Participants with chronic headaches showed a widespread thicker cortex compared with control participants. Mediation analyses revealed that precuneus thickness mediated the relationship of chronic multisite pain and history of suicide attempt. Mediating effects were also identified specific to localized pain, with the strongest effect being amygdala volume in individuals with chronic abdominal pain.

CONCLUSIONS

Results support a widespread effect of chronic pain on brain structure and distinct brain structures underlying chronic musculoskeletal pain, visceral pain, and headaches. Mediation effects of regions in the extended ventromedial prefrontal cortex subsystem suggest that exacerbated negative internal states, negative self-referencing, and impairments in future planning may underlie suicidal behaviors in individuals with chronic pain.

Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data

Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Greater reliance on model-free learning in adolescent anorexia nervosa: An examination of dual-system reinforcement learning

Alterations in learning and decision-making systems are thought to contribute to core features of anorexia nervosa (AN), a psychiatric disorder characterized by persistent dietary restriction and weight loss. Instrumental learning theory identifies a dual-system of habit and goal-directed decision-making, linked to model-free and model-based reinforcement learning algorithms. Difficulty arbitrating between these systems, resulting in an over-reliance on one strategy over the other, has been implicated in compulsivity and extreme goal pursuit, both of which are observed in AN. Characterizing alterations in model-free and model-based systems, and their neural correlates, in AN may clarify mechanisms contributing to symptom heterogeneity (e.g., binge/purge symptoms). This study tested whether adolescents with restricting AN (AN-R; n = 36) and binge/purge AN (AN-BP; n = 20) differentially utilized model-based and model-free learning systems compared to a healthy control group (HC; n = 28) during a Markov two-step decision-making task under conditions of reward and punishment. Associations between model-free and model-based learning and resting-state functional connectivity between neural regions of interest, including orbitofrontal cortex (OFC), nucleus accumbens (NAcc), putamen, and sensory motor cortex (SMC) were examined. AN-R showed higher utilization of model-free learning compared to HC for reward, but attenuated model-free and model-based learning for punishment. In AN-R only, higher model-based learning was associated with stronger OFC-to-left NAcc functional connectivity, regions linked to goal-directed behavior. Greater utilization of model-free learning for reward in AN-R may differentiate this group, particularly during adolescence, and facilitate dietary restriction by prioritizing habitual control in rewarding contexts.

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, nmale /nfemale  = 110/106). The outputs of FreeSurfer's hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; RsegATLAS 2  = 65% vs. RsegFS 2  = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R2  = 25%; p < 10-3 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.

The neural signature of psychomotor disturbance in depression

Up to 70% of patients with major depressive disorder present with psychomotor disturbance (PmD), but at the present time understanding of its pathophysiology is limited. In this study, we capitalized on a large sample of patients to examine the neural correlates of PmD in depression. This study included 820 healthy participants and 699 patients with remitted (n = 402) or current (n = 297) depression. Patients were further categorized as having psychomotor retardation, agitation, or no PmD. We compared resting-state functional connectivity (ROI-to-ROI) between nodes of the cerebral motor network between the groups, including primary motor cortex, supplementary motor area, sensory cortex, superior parietal lobe, caudate, putamen, pallidum, thalamus, and cerebellum. Additionally, we examined network topology of the motor network using graph theory. Among the currently depressed 55% had PmD (15% agitation, 29% retardation, and 11% concurrent agitation and retardation), while 16% of the remitted patients had PmD (8% retardation and 8% agitation). When compared with controls, currently depressed patients with PmD showed higher thalamo-cortical and pallido-cortical connectivity, but no network topology alterations. Currently depressed patients with retardation only had higher thalamo-cortical connectivity, while those with agitation had predominant higher pallido-cortical connectivity. Currently depressed patients without PmD showed higher thalamo-cortical, pallido-cortical, and cortico-cortical connectivity, as well as altered network topology compared to healthy controls. Remitted patients with PmD showed no differences in single connections but altered network topology, while remitted patients without PmD did not differ from healthy controls in any measure. We found evidence for compensatory increased cortico-cortical resting-state functional connectivity that may prevent psychomotor disturbance in current depression, but may perturb network topology. Agitation and retardation show specific connectivity signatures. Motor network topology is slightly altered in remitted patients arguing for persistent changes in depression. These alterations in functional connectivity may be addressed with non-invasive brain stimulation.

What happens to the inhibitory control functions of the right inferior frontal cortex when this area is dominant for language?

A low number of individuals show an atypical brain control of language functions that differs from the typical lateralization in the left cerebral hemisphere. In these cases, the neural distribution of other cognitive functions is not fully understood. Although there is a bias towards a mirrored brain organization consistent with the Causal hypothesis, some individuals are found to be exceptions to this rule. However, no study has focused on what happens to the homologous language areas in the right frontal inferior cortex. Using an fMRI-adapted stop-signal task in a healthy non right-handed sample (50 typically lateralized and 36 atypically lateralized for language production), our results show that atypical lateralization is associated with a mirrored brain organization of the inhibitory control network in the left hemisphere: inferior frontal cortex, presupplementary motor area, and subthalamic nucleus. However, the individual analyses revealed a large number of cases with a noteworthy overlap in the inferior frontal gyrus, which shared both inhibitory and language functions. Further analyses showed that atypical lateralization was associated with stronger functional interhemispheric connectivity and larger corpus callosum. Importantly, we did not find task performance differences as a function of lateralization, but there was an association between atypical dominance in the inferior frontal cortex and higher scores on schizotypy and autistic spectrum traits, as well as worse performance on a reading accuracy test. Together, these results partially support the Causal hypothesis of hemispheric specialization and provide further evidence of the link between atypical hemispheric lateralization and increased interhemispheric transfer through the corpus callosum.

Spatial normalization for voxel-based lesion symptom mapping: impact of registration approaches

Background

Voxel-based lesion symptom mapping (VLSM) assesses the relation of lesion location at a voxel level with a specific clinical or functional outcome measure at a population level. Spatial normalization, that is, mapping the patient images into an atlas coordinate system, is an essential pre-processing step of VLSM. However, no consensus exists on the optimal registration approach to compute the transformation nor are downstream effects on VLSM statistics explored. In this work, we evaluate four registration approaches commonly used in VLSM pipelines: affine (AR), nonlinear (NLR), nonlinear with cost function masking (CFM), and enantiomorphic registration (ENR). The evaluation is based on a standard VLSM scenario: the analysis of statistical relations of brain voxels and regions in imaging data acquired early after stroke onset with follow-up modified Rankin Scale (mRS) values.

Materials and methods

Fluid-attenuated inversion recovery (FLAIR) MRI data from 122 acute ischemic stroke patients acquired between 2 and 3 days after stroke onset and corresponding lesion segmentations, and 30 days mRS values from a European multicenter stroke imaging study (I-KNOW) were available and used in this study. The relation of the voxel location with follow-up mRS was assessed by uni- as well as multi-variate statistical testing based on the lesion segmentations registered using the four different methods (AR, NLR, CFM, ENR; implementation based on the ANTs toolkit).

Results

The brain areas evaluated as important for follow-up mRS were largely consistent across the registration approaches. However, NLR, CFM, and ENR led to distortions in the patient images after the corresponding nonlinear transformations were applied. In addition, local structures (for instance the lateral ventricles) and adjacent brain areas remained insufficiently aligned with corresponding atlas structures even after nonlinear registration.

Conclusions

For VLSM study designs and imaging data similar to the present work, an additional benefit of nonlinear registration variants for spatial normalization seems questionable. Related distortions in the normalized images lead to uncertainties in the VLSM analyses and may offset the theoretical benefits of nonlinear registration.

Impact of prenatal marijuana exposure on adolescent brain structural and functional connectivity and behavioural outcomes

There has been an increase in the number of women using marijuana whilst pregnant. Previous studies have shown that children with prenatal marijuana exposure have developmental deficits in memory and decreased attentiveness. In this study, we assess whether prenatal marijuana exposure is associated with alterations in brain regional morphometry and functional and structural connectivity in adolescents. We downloaded behavioural scores and subject image files from the Adolescent Brain Cognitive DevelopmentSM Study. A total of 178 anatomical and diffusion magnetic resonance imaging files (88 prenatal marijuana exposure and 90 age- and gender-matched controls) and 152 resting-state functional magnetic resonance imaging files (76 prenatal marijuana exposure and 76 controls) were obtained. Behavioural metrics based on the parent-reported child behavioural checklist were also obtained for each subject. The associations of prenatal marijuana exposure with 17 subscales of the child behavioural checklist were calculated. We assessed differences in brain morphometry based on voxel-based and surface-based morphometry in adolescents with prenatal marijuana exposure versus controls. We also evaluated group differences in structural and functional connectivity in adolescents for region-to-region connectivity and graph theoretical metrics. Interactions of prenatal marijuana exposure and graph networks were assessed for impact on behavioural scores. Multiple comparison correction was performed as appropriate. Adolescents with prenatal marijuana exposure had greater abnormal or borderline child behavioural checklist scores in 9 out of 17 subscales. There were no significant differences in voxel- or surface-based morphometry, structural connectivity or functional connectivity between prenatal marijuana exposure and controls. However, there were significant differences in prenatal marijuana exposure-graph network interactions with respect to behavioural scores. There were three structural prenatal marijuana exposure-graph network interactions and seven functional prenatal marijuana exposure-graph network interactions that were significantly associated with behavioural scores. Whilst this study was not able to confirm anatomical or functional differences between prenatal marijuana exposure and unexposed pre-adolescent children, there were prenatal marijuana exposure-brain structural and functional graph network interactions that were significantly associated with behavioural scores. This suggests that altered brain networks may underlie behavioural outcomes in adolescents with prenatal marijuana exposure. More work needs to be conducted to better understand the prognostic value of brain structural and functional network measures in prenatal marijuana exposure.

The cognitive (lateral) hypothalamus

Despite the physiological complexity of the hypothalamus, its role is typically restricted to initiation or cessation of innate behaviors. For example, theories of lateral hypothalamus argue that it is a switch to turn feeding 'on' and 'off' as dictated by higher-order structures that render when feeding is appropriate. However, recent data demonstrate that the lateral hypothalamus is critical for learning about food-related cues. Furthermore, the lateral hypothalamus opposes learning about information that is neutral or distal to food. This reveals the lateral hypothalamus as a unique arbitrator of learning capable of shifting behavior toward or away from important events. This has relevance for disorders characterized by changes in this balance, including addiction and schizophrenia. Generally, this suggests that hypothalamic function is more complex than increasing or decreasing innate behaviors.

Resting state functional connectivity associated with impaired proprioception post-stroke

Deficits in proprioception, the knowledge of limb position and movement in the absence of vision, occur in ~50% of all strokes; however, our lack of knowledge of the neurological mechanisms of these deficits diminishes the effectiveness of rehabilitation and prolongs recovery. We performed resting-state functional magnetic resonance imaging (fMRI) on stroke patients to determine functional brain networks that exhibited changes in connectivity in association with proprioception deficits determined by a Kinarm robotic exoskeleton assessment. Thirty stroke participants were assessed for proprioceptive impairments using a Kinarm robot and underwent resting-state fMRI at 1 month post-stroke. Age-matched healthy control (n = 30) fMRI data were also examined and compared to stroke data in terms of the functional connectivity of brain regions associated with proprioception. Stroke patients exhibited reduced connectivity of the supplementary motor area and the supramarginal gyrus, relative to controls. Functional connectivity of these regions plus primary somatosensory cortex and parietal opercular area was significantly associated with proprioceptive function. The parietal lobe of the lesioned hemisphere is a significant node for proprioception after stroke. Assessment of functional connectivity of this region after stroke may assist with prognostication of recovery. This study also provides potential targets for therapeutic neurostimulation to aid in stroke recovery.