Bayesian analysis of neuroimaging data in FSL

The role of formal schooling in the development of children's reading and arithmetic white matter networks

Children's white matter development is driven by experience, yet it remains poorly understood how it is shaped by attending formal education. A small number of studies compared children before and after the start of formal schooling to understand this, yet they do not allow to separate maturational effects from schooling-related effects. A clever way to (quasi-)experimentally address this issue is the longitudinal school cut-off design, which compares children who are similar in age but differ in schooling (because they are born right before or after the cut-off date for school entry). We used for the first time such a longitudinal school cut-off design to experimentally investigate the effect of schooling on children's white matter networks. We compared "young" first graders (schooling group, n = 34; Mage = 68 months; 20 girls) and "old" preschoolers (non-schooling group, n = 33; Mage = 66 months; 18 girls) that were similar in age but differed in the amount of formal instruction they received. Our study revealed that changes in fractional anisotropy and mean diffusivity in five a priori selected white matter tracts during the transition from preschool to primary school were predominantly driven by age-related maturation. We did not find specific schooling effects on white matter, despite their strong presence for early reading and early arithmetic skills. The present study is the first to disentangle the effects of age-related maturation and schooling on white matter within a longitudinal cohort of 5-year-old preschoolers. RESEARCH HIGHLIGHTS: White matter tracts that have been associated with reading and arithmetic may be susceptible to experience-dependent neuroplasticity when children learn to read and calculate. This longitudinal study used the school cut-off design to isolate schooling-induced from coinciding maturational influences on children's white matter development. White matter changes during the transition from preschool to primary school are predominantly driven by age-related maturation and not by schooling effects. Strong effects of schooling on behavior were shown for early reading and early arithmetic, but not for verbal ability and spatial ability.

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks

BACKGROUND

There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures.

METHODS

MRI data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million.

RESULTS

SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%.

COMPARISON WITH EXISTING METHODS

The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field.

CONCLUSIONS

The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer's disease.

Associations between white matter microstructure and cognitive decline in major depressive disorder versus controls in Germany: a prospective case-control cohort study

BACKGROUND

Cognitive deficits are a key source of disability in individuals with major depressive disorder (MDD) and worsen with disease progression. Despite their clinical relevance, the underlying mechanisms of cognitive deficits remain poorly elucidated, hampering effective treatment strategies. Emerging evidence suggests that alterations in white matter microstructure might contribute to cognitive dysfunction in MDD. We aimed to investigate the complex association between changes in white matter integrity, cognitive decline, and disease course in MDD in a comprehensive longitudinal dataset.

METHODS

In the naturalistic, observational, prospective, case-control Marburg-Münster Affective Disorders Cohort Study, individuals aged 18-65 years and of Caucasian ancestry were recruited from local psychiatric hospitals in Münster and Marburg, Germany, and newspaper advertisements. Individuals diagnosed with MDD and individuals without any history of psychiatric disorder (ie, healthy controls) were included in this subsample analysis. Participants had diffusion-weighted imaging, a battery of neuropsychological tests, and detailed clinical data collected at baseline and at 2 years of follow-up. We used linear mixed-effect models to compare changes in cognitive performance and white matter integrity between participants with MDD and healthy controls. Diffusion-weighted imaging analyses were conducted using tract-based spatial statistics. To correct for multiple comparisons, threshold free cluster enhancement (TFCE) was used to correct α-values at the family-wise error rate (FWE; ptfce-FWE). Effect sizes were estimated by conditional, partial R2 values (sr2) following the Nakagawa and Schielzeth method to quantify explained variance. The association between changes in cognitive performance and changes in white matter integrity was analysed. Finally, we examined whether the depressive disease course between assessments predicted cognitive performance at follow-up and whether white matter integrity mediated this association. People with lived experience were not involved in the research and writing process.

FINDINGS

881 participants were selected for our study, of whom 418 (47%) had MDD (mean age 36·8 years [SD 13·4], 274 [66%] were female, and 144 [34%] were male) and 463 (53%) were healthy controls (mean age 35·6 years [13·5], 295 [64%] were female, and 168 [36%] were male). Baseline assessments were done between Sept 11, 2014, and June 3, 2019, and after a mean follow-up of 2·20 years (SD 0·19), follow-up assessments were done between Oct 6, 2016, and May 31, 2021. Participants with MDD had lower cognitive performance than did healthy controls (p<0·0001, sr2=0·056), regardless of timepoint. Analyses of diffusion-weighted imaging indicated a significant diagnosis × time interaction with a steeper decline in white matter integrity of the superior longitudinal fasciculus over time in participants with MDD than in healthy controls (ptfce-FWE=0·026, sr2=0·002). Furthermore, cognitive decline was robustly associated with the decline in white matter integrity over time across both groups (ptfce-FWE<0·0001, sr2=0·004). In participants with MDD, changes in white matter integrity (p=0·0040, β=0·071) and adverse depressive disease course (p=0·0022, β=-0·073) independently predicted lower cognitive performance at follow-up.

INTERPRETATION

Alterations of white matter integrity occurred over time to a greater extent in participants with MDD than in healthy controls, and decline in white matter integrity was associated with a decline in cognitive performance across groups. Our findings emphasise the crucial role of white matter microstructure and disease progression in depression-related cognitive dysfunction, making both priority targets for future treatment development.

FUNDING

German Research Foundation (DFG).

Prefrontal cortex engagement during an fMRI task of emotion regulation as a potential predictor of treatment response in borderline personality disorder

BACKGROUND

Borderline personality disorder (BPD) is a severe mental illness, with high rates of co-morbid depression and suicidality. Despite the importance of optimizing treatment in BPD, little is known about how neural processes relate to individual treatment response. This study examines how baseline regional brain blood oxygen level dependent (BOLD) activation during a functional magnetic resonance imaging (fMRI) task of emotion regulation is related to treatment response following a six-month randomized clinical trial of Dialectical Behavior Therapy (DBT) or Selective Serotonin Reuptake Inhibitor (SSRI) treatment.

METHODS

Unmedicated females with BPD (N = 37), with recent suicidal behavior or self-injury, underwent an fMRI task in which negative personal memories were presented and they were asked to distance (i.e., downregulate their emotional response) or immerse (i.e., experience emotions freely). Patients were then randomized to DBT (N = 16) or SSRI (N = 21) treatment, with baseline and post-treatment depression and BPD severity assessed.

RESULTS

BOLD activity in prefrontal cortex, anterior cingulate, and insula was associated with distancing. Baseline BOLD during distancing in dorsolateral, ventrolateral, and orbital prefrontal cortex (dlPFC, vlPFC, OFC) differentially predicted depression response across treatment groups, with higher activity predicting better response in the SSRI group, and lower activity predicting better response in the DBT group.

LIMITATIONS

All female samples.

DISCUSSION

Findings indicate that greater prefrontal engagement during emotion regulation may predict more antidepressant benefit from SSRIs, whereas lower engagement may predict better response to DBT. These results suggest different mechanisms of action for SSRI and DBT treatment, and this may allow fMRI to guide individualized treatment selection.

Investigating the feasibility of 18F-flortaucipir PET imaging in the antemortem diagnosis of primary age-related tauopathy (PART): An observational imaging-pathological study

INTRODUCTION

Primary age-related tauopathy (PART) is characterized by neurofibrillary tangles and minimal β-amyloid deposition, diagnosed postmortem. This study investigates 18F-flortaucipir (FTP) PET imaging for antemortem PART diagnosis.

METHODS

We analyzed FTP PET scans from 50 autopsy-confirmed PART and 13 control subjects. Temporal lobe uptake was assessed both qualitatively and quantitatively. Demographic and clinicopathological characteristics and voxel-level uptake using SPM12 were compared between FTP-positive and FTP-negative cases. Intra-reader reproducibility was evaluated with Krippendorff's alpha.

RESULTS

Minimal/mild and moderate FTP uptake was seen in 32% of PART cases and 62% of controls, primarily in the left inferior temporal lobe. No demographic or clinicopathological differences were found between FTP-positive and FTP-negative cases. High intra-reader reproducibility (α = 0.83) was noted.

DISCUSSION

FTP PET imaging did not show a specific uptake pattern for PART diagnosis, indicating that in vivo PART identification using FTP PET is challenging. Similar uptake in controls suggests non-specific uptake in PART.

HIGHLIGHTS

18F-flortaucipir (FTP) PET scans were analyzed for diagnosing PART antemortem. 32% of PART cases had minimal/mild FTP uptake in the left inferior temporal lobe. Similar to PART FTP uptake was found in 62% of control subjects. No specific uptake pattern was found, challenging in vivo PART diagnosis.

Functional activity and connectivity signatures of ketamine and lamotrigine during negative emotional processing: a double-blind randomized controlled fMRI study

Ketamine is a highly effective antidepressant (AD) that targets the glutamatergic system and exerts profound effects on brain circuits during negative emotional processing. Interestingly, the effects of ketamine on brain measures are sensitive to modulation by pretreatment with lamotrigine, which inhibits glutamate release. Examining the antagonistic effects of ketamine and lamotrigine on glutamate transmission holds promise to identify effects of ketamine that are mediated through changes in the glutamatergic system. Investigating this modulation in relation to both the acute and sustained effects of ketamine on functional activity and connectivity during negative emotional processing should therefore provide novel insights. 75 healthy subjects were investigated in a double-blind, single-dose, randomized, placebo-controlled, parallel-group study with three treatment conditions (ketamine, lamotrigine pre-treatment, placebo). Participants completed an emotional face viewing task during ketamine infusion and 24 h later. Acute ketamine administration decreased hippocampal and Default Mode Network (DMN) activity and increased fronto-limbic coupling during negative emotional processing. Furthermore, while lamotrigine abolished the ketamine-induced increase in functional connectivity, it had no acute effect on activity. Sustained (24 h later) effects of ketamine were only found for functional activity, with a significant reduction in the posterior DMN. This effect was blocked by pretreatment with lamotrigine. Our results suggest that both the acute increases in fronto-limbic coupling and the delayed decrease in posterior DMN activity, but not the attenuated limbic and DMN recruitment after ketamine, are mediated by altered glutamatergic transmission.

Functional and free-water imaging in rapid eye movement behaviour disorder and Parkinson's disease

It is established that one of the best predictors of a future diagnosis of Parkinson's disease is a current diagnosis of rapid eye movement behaviour disorder (RBD). In such patients, this provides a unique opportunity to study brain physiology and behavioural motor features of RBD that may precede early-stage Parkinson's disease. Based on prior work in early-stage Parkinson's disease, we aim to determine if the function of corticostriatal and cerebellar regions are impaired in RBD using task-based functional MRI and if structural changes can be detected within the caudate, putamen and substantia nigra in RBD using free-water imaging. To assess motor function, we measured performance on the Purdue Pegboard Test, which is affected in patients with RBD and Parkinson's disease. A cohort of 24 RBD, 39 early-stage Parkinson's disease and 25 controls were investigated. All participants were imaged at 3 Telsa. Individuals performed a unimanual grip force task during functional imaging. Participants also completed scales to assess cognition, sleep and motor symptoms. We found decreased functional activity in both RBD and Parkinson's disease within the motor cortex, caudate, putamen and thalamus compared with controls. There was elevated free-water-corrected fractional anisotropy in the putamen in RBD and Parkinson's disease and elevated free-water in the putamen and posterior substantia nigra in Parkinson's disease compared with controls. Participants with RBD and Parkinson's disease performed significantly worse on all tasks of the Purdue Pegboard Test compared with controls. The both hands task of the Purdue Pegboard Test was most sensitive in distinguishing between groups. A subgroup analysis of early-stage RBD (<2 years diagnosis) confirmed similar findings as those in the larger RBD group. These findings provide new evidence that the putamen is affected in early-stage RBD using both functional and free-water imaging. We also found evidence that the striatum, thalamus and motor cortex have reduced functional activity in early-stage RBD and Parkinson's disease. While the substantia nigra shows elevated free-water in Parkinson's disease, we did not observe this effect in early-stage RBD. These findings point to the corticostriatal and thalamocortical circuits being impaired in RBD patients.

Cognitive Function and Brain Structure in COVID-19 Survivors: The Role of Persistent Symptoms

Persistent COVID-19 symptoms post-acute state have been shown to have a significant negative impact on brain structure and function. In this study, we conducted magnetic resonance imaging (MRI) of the whole brain in 43 working-age adults (mean age: 44.79±10.80; range: 24-65 years) with a history of COVID-19 (731.17±312.41 days post-diagnosis), and also assessed their cognitive function (processing speed, attention, working memory, executive function, and recognition memory), mental health, and sleep quality. MRI data were processed using FSL to derive regional volumes for bilateral nucleus accumbens, caudate, pallidum, putamen, thalamus, amygdala, and hippocampus, and total grey matter, white matter and cerebral spinal fluid volume, and analysed in relation to persistent COVID-19 symptom load, mental health, and sleep quality. Higher persistent COVID-19 symptom load was significantly associated with smaller putamen volume, lower response accuracy on working memory, executive function and recognition memory tasks, as well as a longer time to complete the executive function task, and poorer mental health and sleep quality. Smaller putamen fully mediated the relationship between persistent COVID-19 symptom load and lower executive function. Further research is required to confirm whether reduced putamen volume and its association with poor executive function persists in COVID-19 survivors in the long term.

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This work introduces a novel approach towards creating 3-dimensional (3D) medical foundation models for multimodal neuroimage segmentation through self-supervised training. Our approach involves a novel two-stage pretraining approach using vision transformers. The first stage encodes anatomical structures in generally healthy brains from the large-scale unlabeled neuroimage dataset of multimodal brain magnetic resonance imaging (MRI) images from 41,400 participants. This stage of pertaining focuses on identifying key features such as shapes and sizes of different brain structures. The second pretraining stage identifies disease-specific attributes, such as geometric shapes of tumors and lesions and spatial placements within the brain. This dual-phase methodology significantly reduces the extensive data requirements usually necessary for AI model training in neuroimage segmentation with the flexibility to adapt to various imaging modalities. We rigorously evaluate our model, BrainSegFounder, using the Brain Tumor Segmentation (BraTS) challenge and Anatomical Tracings of Lesions After Stroke v2.0 (ATLAS v2.0) datasets. BrainSegFounder demonstrates a significant performance gain, surpassing the achievements of the previous winning solutions using fully supervised learning. Our findings underscore the impact of scaling up both the model complexity and the volume of unlabeled training data derived from generally healthy brains. Both of these factors enhance the accuracy and predictive capabilities of the model in neuroimage segmentation tasks. Our pretrained models and code are at https://github.com/lab-smile/BrainSegFounder.

The associations among glycemic control, heart variability, and autonomic brain function in healthy individuals: Age- and sex-related differences

INTRODUCTION

The purpose of this study was to clarify the relationships between glycemia and function of the autonomic nervous system (ANS), assessed via resting-state functional connectivity (FC) and heart-rate variability (HRV).

METHODS

Data for this study were extracted from the Leipzig Study for Mind-Body-Emotion Interactions, including 146 healthy adults (114 young, 32 older). Variables of interest were glycated hemoglobin (HbA1c), resting-state FC in the salience aspect of the central-autonomic (S-CAN) and salience network (SN) and HRV (RMSSD and high-frequency HRV (HF-HRV)).

RESULTS

HbA1c was inversely correlated with FC in the S-CAN but not SN. HbA1c was inversely correlated with HRV. Both RMSSD and log(HF-HRV) were correlated with FC in the S-CAN and SN. Age- (not sex-related) differences were observed in the Hb1Ac-FC associations (stronger in older adults) while sex- (not age-related) differences were observed in the HRV-FC (stronger in females).

CONCLUSIONS

These findings extend the diabetes literature to healthy adults in relating glycemia and brain function. The age- and sex-related differences in these relationships highlight the need to account for the potential effects of age and sex in future investigations.

Feasibility of Hologram-Assisted Bilateral Blood-Brain Barrier Opening in Non-Human Primates

Focused ultrasound (FUS) and microbubbles facilitate blood-brain barrier opening (BBBO) noninvasively, transiently, and safely for targeted drug delivery. Unlike state-of-the-art approaches, in this study, we demonstrate for the first time the simultaneous, bilateral BBBO in non-human primates (NHPs) using acoustic holograms at caudate and putamen structures. The simple and low-cost system with a single-element FUS transducer and 3-D printed acoustic hologram was guided by neuronavigation and a robotic arm. The advantages of holograms are transcranial aberration correction, simultaneous multifocus and high localization, and target-independent transducer positioning, defining a promising alternative for time- and cost-efficient FUS procedures. Holograms were designed with the k-space method by time-reversal techniques. T1-weighted MRI was used for treatment planning, while the computed tomography (CT) scan provided the head tissues acoustic properties. For the BBBO procedure, a robotic arm allowed transducer positioning errors below 0.1 mm and 0.1°. Following positioning, 0.5-0.6-MPa, 513-kHz microbubble-enhanced FUS was applied for 4 min. For BBBO assessment, Post-FUS T1-weighted MRI was acquired, and contrast enhancement indicated bilateral gadolinium extravasation at both caudate or putamen structures. The two BBBO locations were separated by 13.13 mm with a volume of 91.81 mm3 in the caudate, compared with 9.40 mm with a volume of 124.52 mm3 in simulation, while they were separated by 21.74 mm with a volume of 145.38 mm3 in the putamen and compared with 22.32 mm with a volume of 156.42 mm3 in simulation. No neurological damage was observed through T2-weighted and susceptibility-weighted imaging. This study demonstrates the feasibility and safety of hologram-assisted neuronavigation-guided-FUS for BBBO in NHP, providing thus an avenue for clinical translation.

Quantification of water exchange across the blood-brain barrier using noncontrast MR fingerprinting

PURPOSE

A method is proposed to quantify cerebral blood volume (v b $$ {v}_b $$ ) and intravascular water residence time (τ b $$ {\tau}_b $$ ) using MR fingerprinting (MRF), applied using a spoiled gradient echo sequence without the need for contrast agent.

METHODS

An in silico study optimized an acquisition protocol to maximize the sensitivity of the measurement tov b $$ {v}_b $$ andτ b $$ {\tau}_b $$ changes. Its accuracy in the presence of variations inT 1 , t $$ {\mathrm{T}}_{1,t} $$ ,T 1 , b $$ {\mathrm{T}}_{1,b} $$ , andB 1 $$ {\mathrm{B}}_1 $$ was evaluated. The optimized protocol (scan time of 19 min) was then tested in a exploratory healthy volunteer study (10 volunteers, mean age 24± $$ \pm $$ 3, six males) at 3 T with a repeat scan taken after repositioning to allow estimation of repeatability.

RESULTS

Simulations show that assuming literature values forT 1 , b $$ {\mathrm{T}}_{1,b} $$ andT 1 , t $$ {\mathrm{T}}_{1,t} $$ , no variation inB 1 $$ {\mathrm{B}}_1 $$ , while fitting onlyv b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , leads to large errors in quantification ofv b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , regardless of noise levels. However, simulations also show that matchingT 1 , t $$ {\mathrm{T}}_{1,t} $$ ,T 1 , b $$ {\mathrm{T}}_{1,b} $$ ,B 1 + $$ {\mathrm{B}}_1^{+} $$ ,v b $$ {v}_b $$ andτ b $$ {\tau}_b $$ , simultaneously is feasible at clinically achievable noise levels. Across the healthy volunteers, all parameter quantifications fell within the expected literature range. In addition, the maps show good agreement between hemispheres suggesting physiologically relevant information is being extracted. Expected differences between white and gray matterT 1 , t $$ {\mathrm{T}}_{1,t} $$ (p < 0.0001) andv b $$ {v}_b $$ (p < 0.0001) are observed,T 1 , b $$ {\mathrm{T}}_{1,b} $$ andτ b $$ {\tau}_b $$ show no significant differences, p = 0.4 and p = 0.6, respectively. Moderate to excellent repeatability was seen between repeat scans: mean intra-class correlation coefficient ofT 1 , t : 0 . 91 $$ {\mathrm{T}}_{1,t}:0.91 $$ ,T 1 , b : 0 . 58 $$ {\mathrm{T}}_{1,b}:0.58 $$ ,v b : 0 . 90 $$ {v}_b:0.90 $$ , andτ b : 0 . 96 $$ {\tau}_b:0.96 $$ .

CONCLUSION

We demonstrate that regional simultaneous quantification ofv b $$ {v}_b $$ ,τ b $$ {\tau}_b $$ ,T 1 , b , T 1 , t $$ {\mathrm{T}}_{1,b},{T}_{1,t} $$ , andB 1 + $$ {\mathrm{B}}_1^{+} $$ using MRF is feasible in vivo.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Functional and structural maturation of auditory cortex from 2 months to 2 years old

BACKGROUND

In school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers.

METHODS AND PARTICIPANTS

The present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (the P2m response, measured using magnetoencephalography, MEG) in a cross-sectional (N = 47, 2 to 24 months, 19 females) as well as longitudinal cohort (N = 18, 2 to 29 months, 8 females) of typically developing infants and toddlers. Of 18 longitudinal infants, 2 infants had data from 3 timepoints and 16 infants had data from 2 timepoints.

RESULTS

In the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. Auditory radiation diffusion accounted for significant variance in P2m latency, even after removing the variance associated with age in both P2m latency and auditory radiation diffusion measures. In the longitudinal sample, latency and FA associations could be observed at the level of a single child.

CONCLUSIONS

Findings provide strong support for the hypothesis that an increase in thalamocortical neural conduction velocity, due to increased axon diameter and/or myelin maturation, contributes to a decrease in the infant P2m auditory evoked response latency.

SIGNIFICANCE

Infant multimodal brain imaging identifies brain mechanisms contributing to the rapid changes in neural circuit activity during the first two years of life.

Concurrent single-pulse (sp) TMS/fMRI to reveal the causal connectome in healthy and patient populations

Neuroimaging and cognitive neuroscience studies have identified neural circuits linked to anxiety, mood, and trauma-related symptoms and focused on their interaction with the medial prefrontal default mode circuitry. Despite these advances, developing new neuromodulatory treatments based on neurocircuitry remains challenging. It remains unclear which nodes within and controlling these circuits are affected and how their impairment is connected to psychiatric symptoms. Concurrent single-pulse (sp) TMS/fMRI offers a promising approach to probing and mapping the integrity of these circuits. In this study, we present concurrent sp-TMS/fMRI data along with structural MRI scans from 152 participants, including both healthy and depressed individuals. The sp-TMS was administered to 11 different cortical sites, providing a dataset that allows researchers to investigate how brain circuits are modulated by spTMS.

Involvement of the cerebellum in structural connectivity enhancement in episodic migraine

BACKGROUND

The pathophysiology of migraine remains poorly understood, yet a growing number of studies have shown structural connectivity disruptions across large-scale brain networks. Although both structural and functional changes have been found in the cerebellum of migraine patients, the cerebellum has barely been assessed in previous structural connectivity studies of migraine. Our objective is to investigate the structural connectivity of the entire brain, including the cerebellum, in individuals diagnosed with episodic migraine without aura during the interictal phase, compared with healthy controls.

METHODS

To that end, 14 migraine patients and 15 healthy controls were recruited (all female), and diffusion-weighted and T1-weighted MRI data were acquired. The structural connectome was estimated for each participant based on two different whole-brain parcellations, including cortical and subcortical regions as well as the cerebellum. The structural connectivity patterns, as well as global and local graph theory metrics, were compared between patients and controls, for each of the two parcellations, using network-based statistics and a generalized linear model (GLM), respectively. We also compared the number of connectome streamlines within specific white matter tracts using a GLM.

RESULTS

We found increased structural connectivity in migraine patients relative to healthy controls with a distinct involvement of cerebellar regions, using both parcellations. Specifically, the node degree of the posterior lobe of the cerebellum was greater in patients than in controls and patients presented a higher number of streamlines within the anterior limb of the internal capsule. Moreover, the connectomes of patients exhibited greater global efficiency and shorter characteristic path length, which correlated with the age onset of migraine.

CONCLUSIONS

A distinctive pattern of heightened structural connectivity and enhanced global efficiency in migraine patients compared to controls was identified, which distinctively involves the cerebellum. These findings provide evidence for increased integration within structural brain networks in migraine and underscore the significance of the cerebellum in migraine pathophysiology.

HOXD12 defines an age-related aggressive subtype of oligodendroglioma

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has highly variable outcomes that are strongly influenced by patient age. The distribution of oligodendroglioma age is non-Gaussian and reportedly bimodal, which motivated our investigation of age-associated molecular alterations that may drive poorer outcomes. We found that elevated HOXD12 expression was associated with both older patient age and shorter survival in the TCGA (FDR < 0.01, FDR = 1e-5) and the CGGA (p = 0.03, p < 1e-3). HOXD12 gene body hypermethylation was associated with older age, higher WHO grade, and shorter survival in the TCGA (p < 1e-6, p < 0.001, p < 1e-3) and with older age and higher WHO grade in Capper et al. (p < 0.002, p = 0.014). In the TCGA, HOXD12 gene body hypermethylation and elevated expression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, MYC activation, and standard histopathological features. Single-nucleus RNA and ATAC sequencing data showed that HOXD12 activity was elevated in neoplastic tissue, particularly within cycling and OPC-like cells, and was associated with a stem-like phenotype. A pan-HOX DNA methylation analysis revealed an age and survival-associated HOX-high signature that was tightly associated with HOXD12 gene body methylation. Overall, HOXD12 expression and gene body hypermethylation were associated with an older, atypically aggressive subtype of oligodendroglioma.

Efficacy Assessment of Cerebral Perfusion Augmentation through Functional Connectivity in an Acute Canine Stroke Model

BACKGROUND AND PURPOSE

Ischemic stroke disrupts functional connectivity within the brain's resting-state networks (RSNs), impacting recovery. This study evaluates the effects of norepinephrine and hydralazine (NEH), a cerebral perfusion augmentation therapy, on RSN integrity in a hyperacute canine stroke model.

MATERIALS AND METHODS

Fifteen adult purpose-bred mongrel canines, divided into treatment and control (natural history) groups, underwent endovascular induction of acute middle cerebral artery occlusion (MCAO). Postocclusion, the treatment group received intra-arterial norepinephrine (0.1-1.52 µg/kg/min, adjusted for 25-45 mm Hg above baseline mean arterial pressure) and hydralazine (20 mg). Resting-state fMRI (rs-fMRI) data were acquired with a 3T scanner by using a blood oxygen level dependent-EPI sequence (TR/TE = 1400 ms/20 ms, 2.5 mm slices, 300 temporal positions). Preprocessing included motion correction, spatial smoothing (2.5 mm full width at half maximum), and high-pass filtering (0.01 Hz cutoff). Functional connectivity within RSNs were analyzed through group-level independent component analysis and weighted whole-brain ROI-to-ROI connectome, pre- and post-MCAO.

RESULTS

NEH therapy significantly maintained connectivity post-MCAO in the higher-order visual and parietal RSNs, as evidenced by thresholded statistical mapping (threshold-free cluster enhancement P corr > .95). However, this preservation was network-dependent, with no significant (P corr < .95) changes in the primary visual and sensorimotor networks.

CONCLUSIONS

NEH demonstrates potential as a proof-of-concept therapy for maintaining RSN functional connectivity after ischemic stroke, emphasizing the therapeutic promise of perfusion augmentation. These insights reinforce the role of functional connectivity as a measurable end point for stroke intervention efficacy, suggesting clinical translatability for patients with insufficient collateral circulation.

Automated brain segmentation and volumetry in dementia diagnostics: a narrative review with emphasis on FreeSurfer

Background

Dementia can be caused by numerous different diseases that present variable clinical courses and reveal multiple patterns of brain atrophy, making its accurate early diagnosis by conventional examinative means challenging. Although highly accurate and powerful, magnetic resonance imaging (MRI) currently plays only a supportive role in dementia diagnosis, largely due to the enormous volume and diversity of data it generates. AI-based software solutions/algorithms that can perform automated segmentation and volumetry analyses of MRI data are being increasingly used to address this issue. Numerous commercial and non-commercial software solutions for automated brain segmentation and volumetry exist, with FreeSurfer being the most frequently used.

Objectives

This Review is an account of the current situation regarding the application of automated brain segmentation and volumetry to dementia diagnosis.

Methods

We performed a PubMed search for “FreeSurfer AND Dementia” and obtained 493 results. Based on these search results, we conducted an in-depth source analysis to identify additional publications, software tools, and methods. Studies were analyzed for design, patient collective, and for statistical evaluation (mathematical methods, correlations).

Results

In the studies identified, the main diseases and cohorts represented were Alzheimer’s disease (n = 276), mild cognitive impairment (n = 157), frontotemporal dementia (n = 34), Parkinson’s disease (n = 29), dementia with Lewy bodies (n = 20), and healthy controls (n = 356). The findings and methods of a selection of the studies identified were summarized and discussed.

Conclusion

Our evaluation showed that, while a large number of studies and software solutions are available, many diseases are underrepresented in terms of their incidence. There is therefore plenty of scope for targeted research.

SEEG4D: a tool for 4D visualization of stereoelectroencephalography data

Epilepsy is a prevalent and serious neurological condition which impacts millions of people worldwide. Stereoelectroencephalography (sEEG) is used in cases of drug resistant epilepsy to aid in surgical resection planning due to its high spatial resolution and ability to visualize seizure onset zones. For accurate localization of the seizure focus, sEEG studies combine pre-implantation magnetic resonance imaging, post-implant computed tomography to visualize electrodes, and temporally recorded sEEG electrophysiological data. Many tools exist to assist in merging multimodal spatial information; however, few allow for an integrated spatiotemporal view of the electrical activity. In the current work, we present SEEG4D, an automated tool to merge spatial and temporal data into a complete, four-dimensional virtual reality (VR) object with temporal electrophysiology that enables the simultaneous viewing of anatomy and seizure activity for seizure localization and presurgical planning. We developed an automated, containerized pipeline to segment tissues and electrode contacts. Contacts are aligned with electrical activity and then animated based on relative power. SEEG4D generates models which can be loaded into VR platforms for viewing and planning with the surgical team. Automated contact segmentation locations are within 1 mm of trained raters and models generated show signal propagation along electrodes. Critically, spatial-temporal information communicated through our models in a VR space have potential to enhance sEEG pre-surgical planning.

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1

BACKGROUND

Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging is more sensitive to white matter damage and better predicts dementia risk than conventional magnetic resonance imaging sequences, such as T1 and fluid attenuation inversion recovery, but diffusion tensor imaging takes longer to acquire and is not routinely available in clinical practice. As diffusion tensor imaging-derived scalar maps-fractional anisotropy (FA) and mean diffusivity (MD)-are frequently used in clinical settings, one solution is to synthesize FA/MD from T1 images.

METHODS

We developed a deep learning model to synthesize FA/MD from T1. The training data set consisted of 4998 participants with the highest white matter hyperintensity volumes in the UK Biobank. Four external validations data sets with small vessel disease were included: SCANS (St George's Cognition and Neuroimaging in Stroke; n=120), RUN DMC (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort; n=502), PRESERVE (Blood Pressure in Established Cerebral Small Vessel Disease; n=105), and NETWORKS (n=26), along with 1000 normal controls from the UK Biobank.

RESULTS

The synthetic maps resembled ground-truth maps (structural similarity index >0.89 for MD maps and >0.80 for FA maps across all external validation data sets except for SCANS). The prediction accuracy of dementia using whole-brain median MD from the synthetic maps is comparable to the ground truth (SCANS ground-truth c-index, 0.822 and synthetic, 0.821; RUN DMC ground truth, 0.816 and synthetic, 0.812) and better than white matter hyperintensity volume (SCANS, 0.534; RUN DMC, 0.710).

CONCLUSIONS

We have developed a fast and generalizable method to synthesize FA/MD maps from T1 to improve the prediction accuracy of dementia in small vessel disease when diffusion tensor imaging data have not been acquired.

Neural pathways associated with reduced rigidity during pallidal deep brain stimulation for Parkinson's disease

Deep brain stimulation (DBS) of the internal segment of the globus pallidus (GPi) can markedly reduce muscle rigidity in people with Parkinson's disease (PD); however, the mechanisms mediating this effect are poorly understood. Computational modeling of DBS provides a method to estimate the relative contributions of neural pathway activations to changes in outcomes. In this study, we generated subject-specific biophysical models of GPi DBS (derived from individual 7-T MRI), including pallidal efferent, putamenal efferent, and internal capsule pathways, to investigate how activation of neural pathways contributed to changes in forearm rigidity in PD. Ten individuals (17 arms) were tested off medication under four conditions: off stimulation, on clinically optimized stimulation, and on stimulation specifically targeting the dorsal GPi or ventral GPi. Quantitative measures of forearm rigidity, with and without a contralateral activation maneuver, were obtained with a robotic manipulandum. Clinically optimized GPi DBS settings significantly reduced forearm rigidity (P < 0.001), which aligned with GPi efferent fiber activation. The model demonstrated that GPi efferent axons could be activated at any location along the GPi dorsal-ventral axis. These results provide evidence that rigidity reduction produced by GPi DBS is mediated by preferential activation of GPi efferents to the thalamus, likely leading to a reduction in excitability of the muscle stretch reflex via overdriving pallidofugal output.NEW & NOTEWORTHY Subject-specific computational models of pallidal deep brain stimulation, in conjunction with quantitative measures of forearm rigidity, were used to examine the neural pathways mediating stimulation-induced changes in rigidity in people with Parkinson's disease. The model uniquely included internal, efferent and adjacent pathways of the basal ganglia. The results demonstrate that reductions in rigidity evoked by deep brain stimulation were principally mediated by the activation of globus pallidus internus efferent pathways.

Long-term effects of an egg-protein hydrolysate on cognitive performance and brain vascular function: a double-blind randomized controlled trial in adults with elevated subjective cognitive failures

PURPOSE

Short-term intake of the egg-protein hydrolysate Newtricious (NWT)-03 improved executive function, but underlying mechanisms and long-term effects, including other cognitive domains, are unknown.

METHODS

A 36-week randomized controlled trial involving 44 overweight/obese individuals experiencing elevated Subjective Cognitive Failures (SCF; aged 60-75 years) assessed the impact of daily consumption of 5.7 g of NWT-03 or placebo powders on cognitive performance (psychomotor speed, executive function, memory) and Cerebral Blood Flow (CBF), a marker of brain vascular function. Cognitive performance was evaluated using a neurophysiological test battery (CANTAB) and CBF was measured using magnetic resonance imaging perfusion method Arterial Spin Labeling (ASL). Serum samples were collected to determine brain-derived neurotrophic factor (BDNF) concentrations.

RESULTS

Anthropometrics, and energy and nutrient intakes remained stable throughout the trial. NWT-03 was well tolerated, and compliance was excellent (median: 99%; range: 87-103%). No overall intervention effects were observed on cognitive performance or CBF, but post-hoc analyses revealed significant improvements on executive function in women, but not men. Specifically, a reduction of 74 ms in reaction latency on the multitasking task (95% CI: -134 to -15; p = 0.02), a reduction of 9 between errors (95%CI: -14 to -3; p < 0.001), and a reduction of 9 total errors (95%CI: -15 to -3; p < 0.001) on the spatial working memory task were found in women. No intervention effects were observed on serum BDNF concentrations (p = 0.31).

CONCLUSION

Long-term consumption of NWT-03 improved multitasking abilities and working memory in women with elevated SCF. Brain vascular function remained unaffected. Sex differences in executive function require additional clarification.

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity

BACKGROUND

Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci.

AIMS

Evaluate discriminative performance and generalizability of functional connectivity markers for BPD.

METHOD

Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data.

RESULTS

Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level.

LIMITATIONS

The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability.

CONCLUSIONS

Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

A Practical Roadmap to Implementing Deep Learning Segmentation in the Clinical Neuroimaging Research Workflow

BACKGROUND

Thanks to the proliferation of open-source tools, we are seeing an exponential growth of machine-learning applications, and its integration has become more accessible, particularly for segmentation tools in neuroimaging.

METHODS

This article explores a generalized methodology that harnesses these tools and aims/enables to expedite and enhance the reproducibility of clinical research. Herein, critical considerations include hardware, software, neural network training strategies, and data labeling guidelines. More specifically, we advocate an iterative approach to model training and transfer learning, focusing on internal validation and outlier handling early in the labeling process and fine-tuning later.

RESULTS

The iterative refinement process allows experts to intervene and improve model reliability while cutting down on their time spent in manual work. A seamless integration of the final model's predictions into clinical research is proposed to ensure standardized and reproducible results.

CONCLUSIONS

In short, this article provides a comprehensive framework for accelerating research using machine-learning techniques for image segmentation.

Hypothalamic MRI-derived microstructure is associated with neurocognitive aging in humans

The hypothalamus regulates homeostasis across the lifespan and is emerging as a regulator of aging. In murine models, aging-related changes in the hypothalamus, including microinflammation and gliosis, promote accelerated neurocognitive decline. We investigated relationships between hypothalamic microstructure and features of neurocognitive aging, including cortical thickness and cognition, in a cohort of community-dwelling older adults (age range 65-97 years, n=124). Hypothalamic microstructure was evaluated with two magnetic resonance imaging diffusion metrics: mean diffusivity (MD) and fractional anisotropy (FA), using a novel image processing pipeline. Hypothalamic MD was cross-sectionally positively associated with age and it was negatively associated with cortical thickness. Hypothalamic FA, independent of cortical thickness, was cross-sectionally positively associated with neurocognitive scores. An exploratory analysis of longitudinal neurocognitive performance suggested that lower hypothalamic FA may predict cognitive decline. No associations between hypothalamic MD, age, and cortical thickness were identified in a younger control cohort (age range 18-63 years, n=99). To our knowledge, this is the first study to demonstrate that hypothalamic microstructure is associated with features of neurocognitive aging in humans.

Structural and functional connectivity in relation to executive functions in antipsychotic-naïve patients with first episode schizophrenia and levels of glutamatergic metabolites

Patients with schizophrenia exhibit structural and functional dysconnectivity but the relationship to the well-documented cognitive impairments is less clear. This study investigates associations between structural and functional connectivity and executive functions in antipsychotic-naïve patients experiencing schizophrenia. Sixty-four patients with schizophrenia and 95 matched controls underwent cognitive testing, diffusion weighted imaging and resting state functional magnetic resonance imaging. In the primary analyses, groupwise interactions between structural connectivity as measured by fixel-based analyses and executive functions were investigated using multivariate linear regression analyses. For significant structural connections, secondary analyses examined whether functional connectivity and associations with executive functions also differed for the two groups. In group comparisons, patients exhibited cognitive impairments across all executive functions compared to controls (p < 0.001), but no group difference were observed in the fixel-based measures. Primary analyses revealed a groupwise interaction between planning abilities and fixel-based measures in the left anterior thalamic radiation (p = 0.004), as well as interactions between cognitive flexibility and fixel-based measures in the isthmus of corpus callosum and cingulum (p = 0.049). Secondary analyses revealed increased functional connectivity between grey matter regions connected by the left anterior thalamic radiation (left thalamus with pars opercularis p = 0.018, and pars orbitalis p = 0.003) in patients compared to controls. Moreover, a groupwise interaction was observed between cognitive flexibility and functional connectivity between contralateral regions connected by the isthmus (precuneus p = 0.028, postcentral p = 0.012), all p-values corrected for multiple comparisons. We conclude that structural and functional connectivity appear to associate with executive functions differently in antipsychotic-naïve patients with schizophrenia compared to controls.

Age-related neural changes underlying long-term recognition of musical sequences

Aging is often associated with decline in brain processing power and neural predictive capabilities. To challenge this notion, we used magnetoencephalography (MEG) and magnetic resonance imaging (MRI) to record the whole-brain activity of 39 older adults (over 60 years old) and 37 young adults (aged 18-25 years) during recognition of previously memorised and varied musical sequences. Results reveal that when recognising memorised sequences, the brain of older compared to young adults reshapes its functional organisation. In fact, it shows increased early activity in sensory regions such as the left auditory cortex (100 ms and 250 ms after each note), and only moderate decreased activity (350 ms) in medial temporal lobe and prefrontal regions. When processing the varied sequences, older adults show a marked reduction of the fast-scale functionality (250 ms after each note) of higher-order brain regions including hippocampus, ventromedial prefrontal and inferior temporal cortices, while no differences are observed in the auditory cortex. Accordingly, young outperform older adults in the recognition of novel sequences, while no behavioural differences are observed with regards to memorised ones. Our findings show age-related neural changes in predictive and memory processes, integrating existing theories on compensatory neural mechanisms in non-pathological aging.

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bi-tensor free water imaging approach

Background: Autism spectrum disorder (ASD) has long been recognized as a lifelong condition, but brain aging studies in autistic adults aged >30 years are limited. Free water, a novel brain imaging marker derived from diffusion MRI (dMRI), has shown promise in differentiating typical and pathological aging and monitoring brain degeneration. We aimed to examine free water and free water corrected dMRI measures to assess white and gray matter microstructure and their associations with age in autistic adults. Methods: Forty-three autistic adults ages 30-73 years and 43 age, sex, and IQ matched neurotypical controls participated in this cross-sectional study. We quantified fractional anisotropy (FA), free water, and free water-corrected FA (fwcFA) across 32 transcallosal white matter tracts and 94 gray matter areas in autistic adults and neurotypical controls. Follow-up analyses assessed age effect on dMRI metrics of the whole brain for both groups and the relationship between dMRI metrics and clinical measures of ASD in regions that significantly differentiated autistic adults from controls. Results: We found globally elevated free water in 24 transcallosal tracts in autistic adults. We identified negligible differences in dMRI metrics in gray matter between the two groups. Age-associated FA reductions and free water increases were featured in neurotypical controls; however, this brain aging profile was largely absent in autistic adults. Additionally, greater autism quotient (AQ) total raw score was associated with increased free water in the inferior frontal gyrus pars orbitalis and lateral orbital gyrus in autistic adults. Limitations: All autistic adults were cognitively capable individuals, minimizing the generalizability of the research findings across the spectrum. This study also involved a cross-sectional design, which limited inferences about the longitudinal microstructural changes of white and gray matter in ASD. Conclusions: We identified differential microstructural configurations between white and gray matter in autistic adults and that autistic individuals present more heterogeneous brain aging profiles compared to controls. Our clinical correlation analysis offered new evidence that elevated free water in some localized white matter tracts may critically contribute to autistic traits in ASD. Our findings underscored the importance of quantifying free water in dMRI studies of ASD.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study

BACKGROUND AND PURPOSE

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia.

MATERIALS AND METHODS

Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed.

RESULTS

Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment.

CONCLUSIONS

The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Fast bias-corrected conductivity mapping using stimulated echoes

OBJECTIVE

To demonstrate the potential of a double angle stimulated echo (DA-STE) method for fast and accurate "full" homogeneous Helmholtz-based electrical properties tomography using a simultaneous B 1 + magnitude and transceive phase measurement.

METHODS

The combination of a spin and stimulated echo can be used to yield an estimate of both B 1 + magnitude and transceive phase and thus provides the means for "full" EPT reconstruction. An interleaved 2D acquisition scheme is used for rapid acquisition. The method was validated in a saline phantom and compared to a double angle method based on two single gradient echo acquisitions (GRE-DAM). The method was evaluated in the brain of a healthy volunteer.

RESULTS

The B 1 + magnitude obtained with DA-STE showed excellent agreement with the GRE-DAM method. Conductivity values based on the "full" EPT reconstruction also agreed well with the expectations in the saline phantom. In the brain, the method delivered conductivity values close to literature values.

DISCUSSION

The method allows the use of the "full" Helmholtz-based EPT reconstruction without the need for additional measurements. As a result, quantitative conductivity values are improved compared to phase-based EPT reconstructions. DA-STE is a fast complex- B 1 + mapping technique that could render EPT clinically relevant at 3 T.

Spatiotemporal whole-brain activity and functional connectivity of melodies recognition

Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.

The relationship between blast-related mild traumatic brain injury and executive function is moderated by white matter integrity

Blast-related mild traumatic brain injury (BR mTBI) is a critical research area in recent combat veterans due to increased prevalence of survived blasts. Post-BR mTBI outcomes are highly heterogeneous and defining neurological differences may help in discrimination and prediction of cognitive outcomes. This study investigates whether white matter integrity, measured with diffusion tensor imaging (DTI), could influence how remote BR mTBI history is associated with executive control. The sample included 151 Veterans from the Minneapolis Veterans Affairs Medical Center who were administered a clinical/TBI assessment, neuropsychological battery, and DTI scan as part of a larger battery. From previous research, six white matter tracts were identified as having a putative relationship with blast severity: the cingulum, hippocampal cingulum, corticospinal tract, inferior fronto-occipital fasciculus, superior longitudinal fasciculus and uncinate. Fractional anisotropy (FA) of the a priori selected white matter tracts and report of BR mTBI were used as predictors of Trail-Making Test B (TMT-B) performance in a multiple linear regression model. Statistical analysis revealed that FA of the hippocampal cingulum moderated the association between report of at least one BR mTBI and poorer TMT-B performance (p < 0.008), such that lower FA value was associated with worse TMT-B outcomes in individuals with BR mTBI. No significant moderation existed for other selected tracts, and the effect was not observed with predictors aside from history of BR mTBI. Investigation at the individual-tract level may lead to a deeper understanding of neurological differences between blast-related and non-blast related injuries.

Muscle diffusion tensor imaging in facioscapulohumeral muscular dystrophy

INTRODUCTION/AIMS

Muscle diffusion tensor imaging has not yet been explored in facioscapulohumeral muscular dystrophy (FSHD). We assessed diffusivity parameters in FSHD subjects compared with healthy controls (HCs), with regard to their ability to precede any fat replacement or edema.

METHODS

Fat fraction (FF), water T2 (wT2), mean, radial, axial diffusivity (MD, RD, AD), and fractional anisotropy (FA) of thigh muscles were calculated in 10 FSHD subjects and 15 HCs. All parameters were compared between FSHD and controls, also exploring their gradient along the main axis of the muscle. Diffusivity parameters were tested in a subgroup analysis as predictors of disease involvement in muscle compartments with different degrees of FF and wT2 and were also correlated with clinical severity scores.

RESULTS

We found that MD, RD, and AD were significantly lower in FSHD subjects than in controls, whereas we failed to find a difference for FA. In contrast, we found a significant positive correlation between FF and FA and a negative correlation between MD, RD, and AD and FF. No correlation was found with wT2. In our subgroup analysis we found that muscle compartments with no significant fat replacement or edema (FF < 10% and wT2 < 41 ms) showed a reduced AD and FA compared with controls. Less involved compartments showed different diffusivity parameters than more involved compartments.

DISCUSSION

Our exploratory study was able to demonstrate diffusivity parameter abnormalities even in muscles with no significant fat replacement or edema. Larger cohorts are needed to confirm these preliminary findings.

Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition

Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology

The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis

OBJECT

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC).

MATERIALS AND METHODS

The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials.

RESULTS

The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps.

DISCUSSION

In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.

Cerebrospinal fluid-based spatial statistics: towards quantitative analysis of cerebrospinal fluid pseudodiffusivity

BACKGROUND

Cerebrospinal fluid (CSF) circulation is essential in removing metabolic wastes from the brain and is an integral component of the glymphatic system. Abnormal CSF circulation is implicated in neurodegenerative diseases. Low b-value magnetic resonance imaging quantifies the variance of CSF motion, or pseudodiffusivity. However, few studies have investigated the relationship between the spatial patterns of CSF pseudodiffusivity and cognition.

METHODS

We introduced a novel technique, CSF-based spatial statistics (CBSS), to automatically quantify CSF pseudodiffusivity in each sulcus, cistern and ventricle. Using cortical regions as landmarks, we segmented each CSF region. We retrospectively analyzed a cohort of 93 participants with varying degrees of cognitive impairment.

RESULTS

We identified two groups of CSF regions whose pseudodiffusivity profiles were correlated with each other: one group displaying higher pseudodiffusivity and near large arteries and the other group displaying lower pseudodiffusivity and away from the large arteries. The pseudodiffusivity in the third ventricle positively correlated with short-term memory (standardized slope of linear regression = 0.38, adjusted p < 0.001) and long-term memory (slope = 0.37, adjusted p = 0.005). Fine mapping along the ventricles revealed that the pseudodiffusivity in the region closest to the start of the third ventricle demonstrated the highest correlation with cognitive performance.

CONCLUSIONS

CBSS enabled quantitative spatial analysis of CSF pseudodiffusivity and suggested the third ventricle pseudodiffusivity as a potential biomarker of cognitive impairment.

Behavioral, Functional Imaging, and Neurophysiological Outcomes of Transcranial Direct Current Stimulation and Speech-Language Therapy in an Individual with Aphasia

Speech-language therapy (SLT) is the most effective technique to improve language performance in persons with aphasia. However, residual language impairments remain even after intensive SLT. Recent studies suggest that combining transcranial direct current stimulation (tDCS) with SLT may improve language performance in persons with aphasia. However, our understanding of how tDCS and SLT impact brain and behavioral relation in aphasia is poorly understood. We investigated the impact of tDCS and SLT on a behavioral measure of scripted conversation and on functional connectivity assessed with multiple methods, both resting-state functional magnetic resonance imaging (rs-fMRI) and resting-state electroencephalography (rs-EEG). An individual with aphasia received 15 sessions of 20-min cathodal tDCS to the right angular gyrus concurrent with 40 min of SLT. Performance during scripted conversation was measured three times at baseline, twice immediately post-treatment, and at 4- and 8-weeks post-treatment. rs-fMRI was measured pre-and post-3-weeks of treatment. rs-EEG was measured on treatment days 1, 5, 10, and 15. Results show that both communication performance and left hemisphere functional connectivity may improve after concurrent tDCS and SLT. Results are in line with aphasia models of language recovery that posit a beneficial role of left hemisphere perilesional areas in language recovery.

Brain state dynamics differ between eyes open and eyes closed rest

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Hierarchical syntax model of music predicts theta power during music listening

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quantify two types of expert-annotated harmonic dependencies throughout a piece of Western classical music: prolongation and preparation. Prolongations can roughly be understood as a musical analogue to linguistic coordination between constituents that share the same function (e.g., 'pizza' and 'pasta' in 'I ate pizza and pasta'). Preparation refers to the dependency between two harmonies whereby the first implies a resolution towards the second (e.g., dominant towards tonic; similar to how the adjective implies the presence of a noun in 'I like spicy … '). Source reconstructed MEG data of sixty-five participants listening to the musical piece was then analysed. We used Bayesian Mixed Effects models to predict theta envelope in the brain, using the number of open prolongation and preparation dependencies as predictors whilst controlling for audio envelope. We observed that prolongation and preparation both carry independent and distinguishable predictive value for theta band fluctuation in key linguistic areas such as the Angular, Superior Temporal, and Heschl's Gyri, or their right-lateralised homologues, with preparation showing additional predictive value for areas associated with the reward system and prediction. Musical expertise further mediated these effects in language-related brain areas. Results show that predictions of precisely formalised music-theoretical models are reflected in the brain activity of listeners which furthers our understanding of the perception and cognition of musical structure.

Mapping short association fibre connectivity up to V3 in the human brain in vivo

Short association fibres (SAF) are the most abundant fibre pathways in the human white matter. Until recently, SAF could not be mapped comprehensively in vivo because diffusion weighted magnetic resonance imaging with sufficiently high spatial resolution needed to map these thin and short pathways was not possible. Recent developments in acquisition hardware and sequences allowed us to create a dedicated in vivo method for mapping the SAF based on sub-millimetre spatial resolution diffusion weighted tractography, which we validated in the human primary (V1) and secondary (V2) visual cortex against the expected SAF retinotopic order. Here, we extended our original study to assess the feasibility of the method to map SAF in higher cortical areas by including SAF up to V3. Our results reproduced the expected retinotopic order of SAF in the V2-V3 and V1-V3 stream, demonstrating greater robustness to the shorter V1-V2 and V2-V3 than the longer V1-V3 connections. The demonstrated ability of the method to map higher-order SAF connectivity patterns in vivo is an important step towards its application across the brain.

Effect of N-acetylcysteine on neural alcohol cue reactivity and craving in adolescents who drink heavily: A preliminary randomized clinical trial

BACKGROUND

Alcohol craving is related to problematic alcohol use; therefore, pharmacotherapies that modulate alcohol craving are of interest. N-acetylcysteine, an over-the-counter antioxidant, is a candidate pharmacotherapy for adolescent alcohol use with the potential to impact craving. Cue-reactivity paradigms using functional magnetic resonance imaging (fMRI) can identify neural regions implicated in craving and serve as a screening tool for novel pharmacotherapy options.

METHODS

This preliminary study examined the effect of N-acetylcysteine on neural reactivity to alcohol cues and subjective craving among 31 non-treatment-seeking adolescents (17.6-19.9 years old, 55% female) who use alcohol heavily. In a randomized cross-over design, participants completed three fMRI sessions: baseline and after a 10-day course of N-acetylcysteine (1200 mg twice daily) and matched placebo. The primary outcome was neural response to alcohol versus non-alcohol beverage cues after N-acetylcysteine versus placebo, with a secondary outcome of self-reported subjective craving.

RESULTS

In the full sample (n = 31), there was no effect of N-acetylcysteine versus placebo on neural alcohol reactivity (ps ≥ 0.49;η p 2 $$ {\upeta_{\mathrm{p}}}^2 $$ s = 0.00-0.07) or self-reported acute alcohol craving (p = 0.18,η p 2 $$ {\upeta_{\mathrm{p}}}^2 $$  = 0.06). However, N-acetylcysteine did reduce self-reported generalized alcohol craving (p = 0.03,η p 2 $$ {\upeta_{\mathrm{p}}}^2 $$  = 0.15). In a subsample of youth who met criteria for past-year alcohol use disorder (n = 19), results remained unchanged.

CONCLUSIONS

N-acetylcysteine may not alter neural reactivity to alcohol cues or acute craving; however, it may reduce general subjective alcohol craving among adolescents who consume alcohol heavily.

Estimation of Cerebral Blood Flow Using the Pulse Wave Amplitude in Brain MRI

RATIONALE AND OBJECTIVES

First, to test the feasibility of cerebral blood flow (CBF) estimation using the pulse wave amplitude in flow-related enhancement (FREE) brain MRI in comparison to pseudo-continuous arterial spin labeling (pCASL-MRI). Second, the potential for acceleration was evaluated retrospectively.

MATERIALS AND METHODS

24 healthy study participants between 20 and 61 years had cerebral MRI. Perfusion imaging was performed with a balanced steady-state free precession sequence for FREE-MRI and with pCASL-MRI for comparison.

RESULTS

The value distribution of the estimated CBF showed a high overlap in the histogram between 0 and 20 mL/100 g/min. However, disparity of the values occurred with more values between 20 and 60 mL/100 g/min using pCASL-MRI and more high values > 60 mL/100 g/min applying FREE-MRI. A Kolmogorov-Smirnov test confirmed a differing probability distribution (P = 0.62). The approximated CBF from FREE-MRI remained stable until only 50% of the acquired data was used. Values from using 40% of the data increased significantly compared to 90% or more (P ≤ 0.05). Values within the white matter presented no significant change after data reduction. The global and voxel-wise correlation coefficients towards pCASL-MRI presented stability during data reduction of FREE-MRI.

CONCLUSION

In conclusion, the proposed technique allows a rough approximation of the CBF compared to pCASL-MRI. Further sequence optimization must be achieved to improve the measurement of relatively lowly perfused tissues. Nevertheless, it offers large potential for imaging speed optimization and enables perfusion-weighted images similarly to the color Doppler mode in ultrasound.

Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aβ+ individuals

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aβ+) individuals, 20 amyloid beta negative (Aβ-) with MCI and 31 Aβ- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aβ+ participants compared to Aβ- MCI and control participants. Within the Aβ+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aβ status or cognition in early-stage AD.

Deep learning reveals lung shape differences on baseline chest CT between mild and severe COVID-19: A multi-site retrospective study

Severe COVID-19 can lead to extensive lung disease causing lung architectural distortion. In this study we employed machine learning and statistical atlas-based approaches to explore possible changes in lung shape among COVID-19 patients and evaluated whether the extent of these changes was associated with COVID-19 severity. On a large multi-institutional dataset (N = 3443), three different populations were defined; a) healthy (no COVID-19), b) mild COVID-19 (no ventilator required), c) severe COVID-19 (ventilator required), and the presence of lung shape differences between them were explored using baseline chest CT. Significant lung shape differences were observed along mediastinal surfaces of the lungs across all severity of COVID-19 disease. Additionally, differences were seen on basal surfaces of the lung when compared between healthy and severe COVID-19 patients. Finally, an AI model (a 3D residual convolutional network) characterizing these shape differences coupled with lung infiltrates (ground-glass opacities and consolidation regions) was found to be associated with COVID-19 severity.

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI

Background and purpose

There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor.

Methods

In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing.

Results

To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco.

Conclusion

Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Analysis of striatal connectivity corresponding to striosomes and matrix in de novo Parkinson's disease and isolated REM behavior disorder

Striosomes and matrix are two compartments that comprise the striatum, each having its own distinct immunohistochemical properties, function, and connectivity. It is currently not clear whether prodromal or early manifest Parkinson's disease (PD) is associated with any striatal matrix or striosomal abnormality. Recently, a method of striatal parcellation using probabilistic tractography has been described and validated, using the distinct connectivity of these two compartments to identify voxels with striosome- and matrix-like connectivity. The goal of this study was to use this approach in tandem with DAT-SPECT, a method used to quantify the level of nigrostriatal denervation, to analyze the striatum in populations of de novo diagnosed, treatment-naïve patients with PD, isolated REM behavioral disorder (iRBD) patients, and healthy controls. We discovered a shift in striatal connectivity, which showed correlation with nigrostriatal denervation. Patients with PD exhibited a significantly higher matrix-like volume and associated connectivity than healthy controls and higher matrix-associated connectivity than iRBD patients. In contrast, the side with less pronounced nigrostriatal denervation in PD and iRBD patients showed a decrease in striosome-like volume and associated connectivity indices. These findings could point to a compensatory neuroplastic mechanism in the context of nigrostriatal denervation and open a new avenue in the investigation of the pathophysiology of Parkinson's disease.

Correlation of altered intrinsic functional connectivity with impaired self-regulation in children and adolescents with ADHD

BACKGROUND

Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring impaired self-regulation (dysregulation), exacerbating adverse outcomes. Neural correlates underlying impaired self-regulation in ADHD remain inconclusive. We aimed to investigate the impact of dysregulation on intrinsic functional connectivity (iFC) in children with ADHD and the correlation of iFC with dysregulation among children with ADHD relative to typically developing controls (TDC).

METHODS

Resting-state functional MRI data of 71 children with ADHD (11.38 ± 2.44 years) and 117 age-matched TDC were used in the final analysis. We restricted our analyses to resting-state networks (RSNs) of interest derived from independent component analysis. Impaired self-regulation was estimated based on the Child Behavioral Checklist-Dysregulation Profile.

RESULTS

Children with ADHD showed stronger iFC than TDC in the left frontoparietal network, somatomotor network (SMN), visual network (VIS), default-mode network (DMN), and dorsal attention network (DAN) (FWE-corrected alpha < 0.05). After adding dysregulation levels as an extra regressor, the ADHD group only showed stronger iFC in the VIS and SMN. ADHD children with high dysregulation had higher precuneus iFC within DMN than ADHD children with low dysregulation. Angular gyrus iFC within DMN was positively correlated with dysregulation in the ADHD group but negatively correlated with dysregulation in the TDC group. Functional network connectivity showed ADHD had a greater DMN-DAN connection than TDC, regardless of the dysregulation level.

CONCLUSIONS

Our findings suggest that DMN connectivity may contribute to impaired self-regulation in ADHD. Impaired self-regulation should be considered categorical and dimensional moderators for the neural correlates of altered iFC in ADHD.

Deep brain stimulation induces white matter remodeling and functional changes to brain-wide networks

Deep brain stimulation (DBS) is an emerging therapeutic option for treatment resistant neurological and psychiatric disorders, most notably depression. Despite this, little is known about the anatomical and functional mechanisms that underlie this therapy. Here we targeted stimulation to the white matter adjacent to the subcallosal anterior cingulate cortex (SCC-DBS) in macaques, modeling the location in the brain proven effective for depression. We demonstrate that SCC-DBS has a selective effect on white matter macro- and micro-structure in the cingulum bundle distant to where stimulation was delivered. SCC-DBS also decreased functional connectivity between subcallosal and posterior cingulate cortex, two areas linked by the cingulum bundle and implicated in depression. Our data reveal that white matter remodeling as well as functional effects contribute to DBS's therapeutic efficacy.