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Proton magnetic resonance spectroscopy (MRS) provides a means of measuring cerebral metabolites relevant to neurodegeneration

Background: Schizophrenia is characterized by self-disturbances, including impaired self-evaluation abilities and source monitoring. The cortical midline structures (e.g., medial prefrontal cortex, anterior and posterior cingulate cortex, and precuneus) and the temporoparietal junction are known to play a key role in self-related processing. In theory, self-disturbances in schizophrenia may arise from impaired activity in these regions. We performed a functional neuroimaging meta-analysis to verify this hypothesis. Methods: A literature search was performed with PubMed and Google Scholar to identify functional neuroimaging studies examining the neural correlates of self-processing in schizophrenia, using self-other or source monitoring paradigms. Fourteen studies were retrieved, involving 245 patients and 201 controls. Using peak coordinates to recreate an effect-size map of contrast results, a standard random-effects variance weighted meta-analysis for each voxel was performed with the Seed-based d Mapping software. Results: During self-processing, decreased activations were observed in schizophrenia patients relative to controls in the bilateral thalamus and the left dorsal anterior cingulate cortex (dACC) and dorso-medial prefrontal cortex. Importantly, results were homogeneous across studies, and no publication bias was observed. Sensitivity analyses revealed that results were replicable in 93–100% of studies. Conclusion: The current results partially support the hypothesized impaired activity of cortical midline brain regions in schizophrenia during self-processing. Decreased activations were observed in the dACC and dorsomedial prefrontal cortex, which are involved in cognitive control and/or salience attribution, as well as decision-making, respectively. These alterations may compromise patients' ability to direct their attention toward themselves and/or others and to make the decision whether a certain trait applies to one's self or to someone else. In addition, decreased activations were observed in the thalamus, which is not a core region of the default-mode network, and is involved in information integration. These thalamic alterations may compromise self-coherence in schizophrenia.

Background: Affective dysregulation and impaired cognitive control are implicated in the pathology of functional neurological disorders (FNDs). However, voluntary regulation of emotions has seldom been researched in this group of patients. We hypothesized that patients with FNDs use inefficient voluntary emotion regulation strategies and regulate emotional reactions via increased motor activation. Methods: Fifteen patients with functional movement disorder (FMD) and fifteen healthy subjects matched by age, sex, and education underwent an emotion regulation task in fMRI. For stimuli, we used neutral and negative pictures from the International Affective Picture System. There was no restriction on their emotion regulation strategy. Both patients and healthy subjects were asked about the strategies they had used in a post-scanning interview. Participant levels of depression, trait anxiety, and alexithymia were assessed. Results: There were no significant differences in the emotion regulation strategies used by patients and healthy subjects, nor in levels of reported alexithymia and depression. However, patients showed increased activation in several brain areas when observing negative pictures, notably in the post-central gyrus, precuneus, posterior cingulate cortex (PCC) and cerebellar vermis, and also in their emotion regulation condition, particularly in the precuneus and post-central gyrus. Alexithymia was negatively associated with left insular activation during the observation of unpleasant stimuli only in the patient group. Conclusions: Our findings may implicate areas associated with self-referential processing in voluntary emotional regulation and lower emotional awareness as having a role in patients with functional movement disorders. However, our findings must be replicated with larger sample.

ObjectiveEpilepsy is associated with both changes in brain connectivity and memory function, usually studied in the chronic patients. The aim of this study was to explore the presence of connectivity alterations measured by EEG in the parietofrontal network in patients with temporal lobe epilepsy (TLE), and to examine episodic memory, at the time point of diagnosis.MethodsThe parietofrontal network of newly diagnosed patients with TLE (N = 21) was assessed through electroencephalography (EEG) effective connectivity and compared with that of matched controls (N = 21). Furthermore, we assessed phenomenological aspects of episodic memory in both groups. Association between effective connectivity and episodic memory were assessed through correlation.ResultsPatients with TLE displayed decreased episodic (p ≤ 0.001, t = −5.18) memory scores compared with controls at the time point of diagnosis. The patients showed a decreased right parietofrontal connectivity (p = 0.03, F = 4.94) compared with controls, and significantly weaker connectivity in their right compared with their left hemisphere (p = 0.008, t = −2.93). There were no significant associations between effective connectivity and episodic memory scores.ConclusionsWe found changes in both memory function and connectivity at the time point of diagnosis, supporting the notion that TLE involves complex memory functions and brain networks beyond the seizure focus to strongly interconnected brain regions, already early in the disease course. Whether the observed connectivity changes can be interpreted as functionally important to the alterations in memory function, it remains speculative.

IntroductionThe long-term impact of COVID-19 on brain function remains poorly understood, despite growing concern surrounding post-acute COVID-19 syndrome (PACS). The goal of this cross-sectional, observational study was to determine whether there are significant alterations in resting brain function among non-hospitalized individuals with PACS, compared to symptomatic individuals with non-COVID infection.MethodsData were collected for 51 individuals who tested positive for COVID-19 (mean age 41±12 yrs., 34 female) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19 (mean age 41±14 yrs., 9 female), with both groups assessed an average of 4-5 months after COVID testing. None of the participants had prior neurologic, psychiatric, or cardiovascular illness. Resting brain function was assessed via functional magnetic resonance imaging (fMRI), and self-reported symptoms were recorded.ResultsIndividuals with COVID-19 had lower temporal and subcortical functional connectivity relative to controls. A greater number of ongoing post-COVID symptoms was also associated with altered functional connectivity between temporal, parietal, occipital and subcortical regions.DiscussionThese results provide preliminary evidence that patterns of functional connectivity distinguish PACS from non-COVID infection and correlate with the severity of clinical outcome, providing novel insights into this highly prevalent disorder.

Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.

Those of African American or Latin American descent have been demonstrated to have more severe clinical presentations of multiple sclerosis (MS) than non-Latin American White people with MS. Concurrently, radiological burden of disease on magnetic resonance imaging (MRI) in African Americans with MS has also been described as being more aggressive. Here, we review MRI studies in diverse racial and ethnic groups (adult and pediatric) investigating lesion burden, inflammation, neurodegeneration, and imaging response to disease modifying therapy. We also discuss why such disparities may exist beyond biology, and how future studies may provide greater insights into underlying differences.