White matter tracts associated with alexithymia and emotion regulation: A diffusion MRI study

White Matter Correlates of Cognition: A Diffusion Magnetic Resonance Imaging Study

BACKGROUND

Our comprehension of the interplay of cognition and the brain remains constrained. While functional imaging studies have identified cognitive brain regions, structural correlates of cognitive functions remain underexplored. Advanced methods like Diffusion Magnetic Resonance Imaging (DMRI) facilitate the exploration of brain connectivity and White Matter (WM) tract microstructure. Therefore, we conducted connectometry method on DMRI data, to reveal WM tracts associated with cognition.

METHODS

125 healthy participants from the National Institute of Mental Health Intramural Healthy Volunteer Dataset were recruited. Multiple regression analyses were conducted between DMRI-derived Quantitative Anisotropy (QA) values within WM tracts and scores of participants in Flanker Inhibitory Control and Attention Test (attention), Dimensional Change Card Sort (executive function), Picture Sequence Memory Test (episodic memory), and List Sorting Working Memory Test (working memory) tasks from National Institute of Health toolbox. The significance level was set at False Discovery Rate (FDR)<0.05.

RESULTS

We identified significant positive correlations between the QA of WM tracts within the left cerebellum and bilateral fornix with attention, executive functioning, and episodic memory (FDR=0.018, 0.0002, and 0.0002, respectively), and a negative correlation between QA of WM tracts within bilateral cerebellum with attention (FDR=0.028). Working memory demonstrated positive correlations with QA of left inferior longitudinal and left inferior fronto-occipital fasciculi (FDR=0.0009), while it showed a negative correlation with QA of right cerebellar tracts (FDR=0.0005).

CONCLUSION

Our results underscore the intricate link between cognitive performance and WM integrity in frontal, temporal, and cerebellar regions, offering insights into early detection and targeted interventions for cognitive disorders.

One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer

The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.

Functional and structural lesion network mapping in neurological and psychiatric disorders: a systematic review

Background

The traditional approach to studying the neurobiological mechanisms of brain disorders and localizing brain function involves identifying brain abnormalities and comparing them to matched controls. This method has been instrumental in clinical neurology, providing insight into the functional roles of different brain regions. However, it becomes challenging when lesions in diverse regions produce similar symptoms. To address this, researchers have begun mapping brain lesions to functional or structural networks, a process known as lesion network mapping (LNM). This approach seeks to identify common brain circuits associated with lesions in various areas. In this review, we focus on recent studies that have utilized LNM to map neurological and psychiatric symptoms, shedding light on how this method enhances our understanding of brain network functions.

Methods

We conducted a systematic search of four databases: PubMed, Scopus, and Web of Science, using the term "Lesion network mapping." Our focus was on observational studies that applied lesion network mapping in the context of neurological and psychiatric disorders.

Results

Following our screening process, we included 52 studies, comprising a total of 6,814 subjects, in our systematic review. These studies, which utilized functional connectivity, revealed several regions and network overlaps across various movement and psychiatric disorders. For instance, the cerebellum was found to be part of a common network for conditions such as essential tremor relief, parkinsonism, Holmes tremor, freezing of gait, cervical dystonia, infantile spasms, and tics. Additionally, the thalamus was identified as part of a common network for essential tremor relief, Holmes tremor, and executive function deficits. The dorsal attention network was significantly associated with fall risk in elderly individuals and parkinsonism.

Conclusion

LNM has proven to be a powerful tool in localizing a broad range of neuropsychiatric, behavioral, and movement disorders. It holds promise in identifying new treatment targets through symptom mapping. Nonetheless, the validity of these approaches should be confirmed by more comprehensive prospective studies.

White matter tracts alterations underpinning reward and conflict processing

BACKGROUND

Reinforcement sensitivity theory (RST) is proposed as a neurobiological system that eventually led to emotion and motivation-based constructs of personality. Traditionally segmented into the behavioral activation system (BAS) and the behavioral inhibition system (BIS), RST is commonly used to describe personality and behavior. Although there have been studies linking gray matter alterations with BIS/BAS subscales, the role of white matter (WM) alterations is yet controversial. We aimed to investigate the specific WM tracts associated with BIS/BAS scores.

METHODS

220 healthy participants (mean age = 39.14 ± 20.23, 80 (35.7 %) females) were evaluated using the BIS/BAS questionnaire from the LEMON database. Diffusion MRI connectometry (DMRI) was used to investigate the WM correlates of BIS/BAS subscales in each gender group. Multiple regression models with the covariates of age, handedness, and education were fitted to address the correlation of local connectomes with BIS/BAS components.

RESULTS

DMRI connectometry revealed that the quantitative anisotropy (QA) value of the splenium of the corpus callosum, right cerebellum, middle cerebellar peduncle, and superior cerebellar peduncle, had a significant negative correlation with each BIS/BAS subscale. In contrast, the QA value in the body of the corpus callosum and bilateral cingulum showed a positive correlation with BIS/BAS subscales.

CONCLUSION

The connectivity of WM in certain tracts may contribute to behavioral activation and inhibition. This finding expands the findings on the neural networks associated with risk-taking and reward-seeking behaviors.

The impact of loneliness and social adaptation on depressive symptoms: Behavioral and brain measures evidence from a brain health perspective

Introduction

Early detection of depression is a cost-effective way to prevent adverse outcomes on brain physiology, cognition, and health. Here we propose that loneliness and social adaptation are key factors that can anticipate depressive symptoms.

Methods

We analyzed data from two separate samples to evaluate the associations between loneliness, social adaptation, depressive symptoms, and their neural correlates.

Results

For both samples, hierarchical regression models on self-reported data showed that loneliness and social adaptation have negative and positive effects on depressive symptoms. Moreover, social adaptation reduces the impact of loneliness on depressive symptoms. Structural connectivity analysis showed that depressive symptoms, loneliness, and social adaptation share a common neural substrate. Furthermore, functional connectivity analysis demonstrated that only social adaptation was associated with connectivity in parietal areas.

Discussion

Altogether, our results suggest that loneliness is a strong risk factor for depressive symptoms while social adaptation acts as a buffer against the ill effects of loneliness. At the neuroanatomical level, loneliness and depression may affect the integrity of white matter structures known to be associated to emotion dysregulation and cognitive impairment. On the other hand, socio-adaptive processes may protect against the harmful effects of loneliness and depression. Structural and functional correlates of social adaptation could indicate a protective role through long and short-term effects, respectively. These findings may aid approaches to preserve brain health via social participation and adaptive social behavior.

The Association of White Matter Tracts with Alexithymia among Individuals with Major Depressive Disorder

Alexithymia is characterized by impairments in the processing of emotions. Although the disruptions in the white matter (WM) integrity in Major depressive disorder (MDD) has frequently been reported, the underlying relationship with alexithymia remains unclear. In the present study, we investigated WM tracts with Tracts Constrained by UnderLying Anatomy approach to discover potential associations between alexithymia and WM integrity to identify the neural basis of impaired emotional self-awareness in MDD. 101 patients with MDD and 99 healthy sex- and age-matched individuals underwent diffusion-weighted imaging. All participants were assessed with the 20-item Toronto Alexithymia Scale (TAS). TAS scores were significantly higher in MDD patients than in controls. Patients with MDD exhibited significantly lower FA values in the left inferior longitudinal fasciculus and it also showed negative associations with TAS. These results contribute to the neurobiological evidence on the association between MDD and alexithymia. Additionally, they suggest that reduced white matter integrity in the regions constitutes a principal pathophysiology underlying impaired emotional recognition and description in MDD.