Dementia is an escalating global health challenge, with Alzheimer's disease (AD) at its forefront. Substantial evidence highlights the accumulation of AD-related pathological proteins in specific brain regions and their subsequent dissemination throughout the broader area along the brain network, leading to disruptions in both individual brain regions and their interconnections. Although a comprehensive understanding of the neurodegeneration-brain network link is lacking, it is undeniable that brain networks play a pivotal role in the development and progression of AD. To thoroughly elucidate the intricate network of elements and connections constituting the human brain, the concept of the brain connectome was introduced. Research based on the connectome holds immense potential for revealing the mechanisms underlying disease development, and it has become a prominent topic that has attracted the attention of numerous researchers. In this review, we aim to systematically summarize studies on brain networks within the context of AD, critically analyze the strengths and weaknesses of existing methodologies, and offer novel perspectives and insights, intending to serve as inspiration for future research.
Background: Parkinson's disease (PD) patients suffer from progressive gray matter volume (GMV) loss, but whether distinct patterns of atrophy progression exist within PD are still unclear.
Objective: This study aims to identify PD subtypes with different rates of GMV loss and assess their association with clinical progression.
Methods: This study included 107 PD patients (mean age: 60.06 ± 9.98 years, 70.09% male) with baseline and ≥ 3-year follow-up structural MRI scans. A linear mixed-effects model was employed to assess the rates of regional GMV loss. Hierarchical cluster analysis was conducted to explore potential subtypes based on individual rates of GMV loss. Clinical score changes were then compared across these subtypes.
Results: Two PD subtypes were identified based on brain atrophy rates. Subtype 1 (n = 63) showed moderate atrophy, notably in the prefrontal and lateral temporal lobes, while Subtype 2 (n = 44) had faster atrophy across the brain, particularly in the lateral temporal region. Furthermore, subtype 2 exhibited faster deterioration in non-motor (MDS-UPDRS-Part Ⅰ, β = 1.26 ± 0.18, P = 0.016) and motor (MDS-UPDRS-Part Ⅱ, β = 1.34 ± 0.20, P = 0.017) symptoms, autonomic dysfunction (SCOPA-AUT, β = 1.15 ± 0.22, P = 0.043), memory (HVLT-Retention, β = −0.02 ± 0.01, P = 0.016) and depression (GDS, β = 0.26 ± 0.083, P = 0.019) compared to subtype 1.
Conclusion: The study has identified two PD subtypes with distinct patterns of atrophy progression and clinical progression, which may have implications for developing personalized treatment strategies.
Background: Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.
Objective: We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.
Methods: We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Multi-scale data fusion was performed using a machine learning integration framework, together with several conventional machine learning methods and neural networks for patient classification.
Results: We found that multi-omics data fusion in conventional machine learning models achieved the highest accuracy (AUC ~0.76-0.92) in contrast to the single-modality models, with AUC improvements of 8.88 to 22.64%. Similar findings were observed for the neural network, showing an increase of 16.57% for the multimodal classification model (accuracy 71.43%) compared to the single-modal average. In addition, we identified several brain regions in the left posterior cingulate and right frontal pole that made a major contribution to disease classification.
Conclusion: We provide empirical evidence for the increased accuracy achieved by imaging genetic data integration in schizophrenia classification. Multi-scale data fusion holds promise for enhancing diagnostic precision, facilitating early detection and personalizing treatment regimens in schizophrenia.
The brain controls the nerve system, allowing complex emotional and cognitive activities. The microbiota-gut-brain axis is a bidirectional neural, hormonal, and immune signaling pathway that could link the gastrointestinal tract to the brain. Over the past few decades, gut microbiota has been demonstrated to be an essential component of the gastrointestinal tract that plays a crucial role in regulating most functions of various body organs. The effects of the microbiota on the brain occur through the production of neurotransmitters, hormones, and metabolites, regulation of host-produced metabolites, or through the synthesis of metabolites by the microbiota themselves. This affects the host's behavior, mood, attention state, and the brain's food reward system. Meanwhile, there is an intimate association between the gut microbiota and exercise. Exercise can change gut microbiota numerically and qualitatively, which may be partially responsible for the widespread benefits of regular physical activity on human health. Functional magnetic resonance imaging (fMRI) is a non-invasive method to show areas of brain activity enabling the delineation of specific brain regions involved in neurocognitive disorders. Through combining exercise tasks and fMRI techniques, researchers can observe the effects of exercise on higher brain functions. However, exercise's effects on brain health via gut microbiota have been little studied. This article reviews and highlights the connections between these three interactions, which will help us to further understand the positive effects of exercise on brain health and provide new strategies and approaches for the prevention and treatment of brain diseases.
Whereas autism spectrum condition is known for its social and communicative challenges, some autistic children demonstrate unusual islets of abilities including those related to mathematics, the neurobiological underpinnings of which are increasingly becoming the focus of research. Here we describe an 8-year-old autistic boy with intellectual and language challenges, yet exceptional arithmetic ability. He can perform verbal-based multiplication of three- and even four-digit numbers within 20 seconds. To gain insights into the neural basis of his talent, we investigated the gray matter in the child's brain in comparison to typical development, applying voxel-based morphometry to magnetic resonance imaging data. The case exhibited reduced gray matter volume in regions associated with arithmetic, which may suggest an accelerated development of brain regions with arithmetic compared to typically developing individuals: potentially a key factor contributing to his exceptional talent. Taken together, this case report describes an example of the neurodiversity of autism. Our research provides valuable insights into the potential neural basis of exceptional arithmetic abilities in individuals with the autism spectrum and its potential contribution to depicting the diversity and complexity of autism.
Background: Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety.
Objective: This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety.
Methods: Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety.
Results: Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus. Further, the left SFG fALFF was positively correlated with social anxiety; brain-personality-symptom analysis revealed that this relationship was mediated by social intelligence.
Conclusion: These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have implication for developing neurobehavioral interventions to mitigate social anxiety.
The clinical manifestations of adult-acquired cerebellar diseases often surpass those of congenital cerebellar diseases, suggesting the significant role of the cerebellum in the developing brain. Moreover, emerging evidence from structural and functional magnetic resonance imaging indicates that the cerebellum is implicated not only in motor functions but also in non-motor domains such as cognition, emotion, and language. However, delineating the specific extent of cerebellar development required to prevent deficits in either motor or non-motor functions remains challenging. In this study, we present two new cases of unilateral cerebellar agenesis. One individual leads a nearly normal life, while the other exhibits mild cognitive impairment, mild depression, and severe autism, but maintains normal motor function. Van der Heijden et al. (2023) revealed that the brain can compensate for some, but not all, perturbations to the developing cerebellum, including motor deficits and impairments in social behaviors. Therefore, we hypothesize that comparing structural images from our patients and reviewing pertinent literature may elucidate the reasons for the varied clinical manifestations observed in patients with cerebellar agenesis.
High magnetic field homogeneity is critical for magnetic resonance imaging (MRI), functional MRI, and magnetic resonance spectroscopy (MRS) applications. B0 inhomogeneity during MR scans is a long-standing problem resulting from magnet imperfections and site conditions, with the main issue being the inhomogeneity across the human body caused by differences in magnetic susceptibilities between tissues, resulting in signal loss, image distortion, and poor spectral resolution. Through a combination of passive and active shim techniques, as well as technological advances employing multi-coil techniques, optimal coil design, motion tracking, and real-time modifications, improved field homogeneity and image quality have been achieved in MRI/MRS. The integration of RF and shim coils brings a high shim efficiency due to the proximity of participants. This technique will potentially be applied to high-density RF coils with a high-density shim array for improved B0 homogeneity. Simultaneous shimming and image encoding can be achieved using multi-coil array, which also enables the development of novel encoding methods using advanced magnetic field control. Field monitoring enables the capture and real-time compensation for dynamic field perturbance beyond the static background inhomogeneity. These advancements have the potential to better use the scanner performance to enhance diagnostic capabilities and broaden applications of MRI/MRS in a variety of clinical and research settings. The purpose of this paper is to provide an overview of the latest advances in B0 magnetic field shimming and magnetic field control techniques as well as MR hardware, and to emphasize their significance and potential impact on improving the data quality of MRI/MRS.
Background: With bipolar disorder (BD) having a lifetime prevalence of 4.4% and a significant portion of patients being chronically burdened by symptoms, there has been an increased focus on uncovering new targets for intervention in BD. One area that has shown early promise is the mitochondrial hypothesis. However, at the time of publication no studies have utilized positron emission tomography (PET) imaging to assess mitochondrial function in the setting of BD.
Case Presentation: Our participant is a 58 year-old male with a past medical history notable for alcohol use disorder and BD (unspecified type) who underwent PET imaging with the mitochondrial complex I PET ligand 18F-BCPP-EF. The resulting images demonstrated significant overlap between areas of dysfunction identified with the 18F-BCPP-EF PET ligand and prior functional magnetic resonance imaging (MRI) techniques in the setting of BD. That overlap was seen in both affective and cognitive circuits, with mitochondrial dysfunction in the fronto-limbic, ventral affective, and dorsal cognitive circuits showing particularly significant differences.
Conclusions: Despite mounting evidence implicating mitochondria in BD, this study represents the first PET imaging study to investigate this mechanistic connection. There were key limitations in the form of comorbid alcohol use disorder, limited statistical power inherent to a case study, no sex matched controls, and the absence of a comprehensive psychiatric history. However, even with these limitations in mind, the significant overlap between dysfunction previously demonstrated on functional MRI and this imaging provides compelling preliminary evidence that strengthens the mechanistic link between mitochondrial dysfunction and BD.
Schizophrenia is a complex disorder characterized by multiple neurochemical abnormalities and structural changes in the brain. These abnormalities may begin before recognizable clinical symptoms appear and continue as a dynamic process throughout the illness. Recent advances in imaging techniques have significantly enriched our comprehension of these structural alterations, particularly focusing on gray and white matter irregularities and prefrontal, temporal, and cingulate cortex alterations. Some of the changes suggest treatment resistance to antipsychotic medications, while treatment nonadherence and relapses may further exacerbate structural abnormalities. This narrative review aims to discuss the literature about alterations and deficits within the brain, which could improve the understanding of schizophrenia and how to interpret neurostructural changes.
Background: We reported a case of cervical invasive vagus nerve stimulation (iVNS) treatment for avoidant/restrictive food intake disorder (ARFID) in a patient with severe anxiety and depression. This patient was even given a critical illness notice during his hospitalization and all treatment efforts were failed.
Objective: We aimed to verfiy the effectiveness of iVNS in a patient with ARFID.
Methods: We first attempted to perform cervical iVNS in this case and then observed the changes in clinical scores. We also analyzed the alterations in brain magnetic resonance imaging characteristics before and after iVNS using multi-modal neuroimagings.
Results: After 18 days of iVNS (from 1 to 19 July 2023), the patient's clinical symptoms improved significantly and he rapidly gained 5 kg in weight. The brain functional characteristics of this patient tended toward those of the normal group. Functional connectivities of the medial of orbitalis prefrontal cortex returned to the normal range after iVNS.
Conclusion: This is a precedent for performing cervical iVNS in an ARFID patient. Brain neural activity can be modulated through iVNS. The observed improvements in clinical scores and positive changes in brain function validated the effectiveness of iVNS. This case study provides evidence that this intervention technique could be used to reduce the burden on more similar ARFID patients.
Background: While cognitive reappraisal represents a promising emotion regulation strategy in regulating basic emotions, little experimental research has investigated its efficacy in reducing self-conscious emotions such as shame and guilt.
Objective: The present study aimed to investigate the effectiveness of detached reappraisal and positive reappraisal in regulating feelings of shame and guilt, and also compared the effectiveness of these two strategies using behavioral and event-related potentials.
Method: Thirty-nine participants grouped either in positive reappraisal or detached reappraisal condition were informed to advise the decider to perform a dot-estimation task. Participants were also informed that the payment of the decider would be reduced if he/she adopted the wrong advice provided by them.
Result: The behavioral results demonstrated that both regulation strategies reduced shame and guilt when compared to the observation stage. We also observed a phenomenon (absent during the regulation of shame) where regulating guilt resulted in a higher parietal P3 amplitude, a component related to negative experiences, compared to the observation phase in the detached reappraisal group.
Conclusion: The results demonstrated that both regulation strategies were able to regulate self-conscious emotions (shame, guilt) effectively. The findings of this study enhance our understanding of the neurophysiological effects of different regulation strategies on self-conscious emotions.
Background: Naturalistic stimuli, such as videos, can elicit complex brain activations. However, the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations, particularly for higher-level processes such as social interactions.
Objective: We hypothesized that activations in different layers of a convolutional neural network (VGG-16) would correspond to varying levels of brain activation, reflecting the brain's visual processing hierarchy. Additionally, we aimed to explore which brain regions would be linked to the deeper layers of the network.
Methods: This study analyzed functional MRI data from participants watching a cartoon video. Using a pre-trained VGG-16 convolutional neural network, we mapped hierarchical features of the video to different levels of brain activation. Activation maps from various kernels and layers were extracted from video frames, and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain responses.
Results: Lower layers of the network were primarily associated with activations in lower visual regions, although some kernels also unexpectedly showed associations with the posterior cingulate cortex. Deeper layers were linked to more anterior and lateral regions of the visual cortex, as well as the supramarginal gyrus.
Conclusions: This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions, providing valuable insights into how different brain regions respond to varying levels of visual processing.
Posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) are common stress-related psychiatric disorders. Genetic and neurobiology research has supported the viewpoint that PTSD and MDD may possess common and disorder-specific underlying mechanisms. In this systematic review, we summarize evidence for the similarities and differences in brain functional and structural features of MDD, PTSD, and their comorbidity, as well as the effects of extensively used therapies in patients with comorbid PTSD and MDD (PTSD + MDD). These functional magnetic resonance imaging (MRI) studies highlight the (i) shared hypoactivation in the prefrontal cortex during cognitive and emotional processing in MDD and PTSD; (ii) higher activation in fear processing regions including amygdala, hippocampus, and insula in PTSD compared to MDD; and (iii) distinct functional deficits in brain regions involved in fear and reward processing in patients with PTSD + MDD relative to those with PTSD alone. These structural MRI studies suggested that PTSD and MDD share features of reduced volume in focal frontal areas. The treatment effects in patients with PTSD + MDD may correlate with the normalization trend of structural alterations. Neuroimaging predictors of repetitive transcranial magnetic stimulation response in patients with PTSD + MDD may differ from the mono-diagnostic groups. In summary, neuroimaging studies to date have provided limited information about the shared and disorder-specific features in MDD and PTSD. Further research is essential to pave the way for developing improved diagnostic markers and eventually targeted treatment approaches for the shared and distinct brain alterations presented in patients with MDD and PTSD.
From July 20 to 22, 2024, the ISMRM Endorsed Workshop on MR for Psychiatry was held in Chengdu City, China. This prestigious event attracted numerous academic elites worldwide, and Professor Benjamin Becker from the University of Hong Kong was invited. On the morning of July 20, during the “Advances in MR Technology” session, Professor Becker delivered an engaging lecture entitled “Novel approaches to precision MRI-imaging of human emotion.” His presentation was met with great enthusiasm and sparked lively discussions among the participants. Following the conference, the Psychoradiology journal interviewed Professor Becker. In the interview, Benjamin emphasized the significant role of interdisciplinary collaboration, spanning various fields including psychology, neuroscience, clinical medicine, biomedical engineering, and computer science. Professor Becker firmly believed that such collaboration was crucial for a deeper understanding of the brain and psychiatric disorders. Additionally, he highly valued the importance of international cooperation, especially in addressing global mental health issues and challenges related to psychiatric disorders.
The prefrontal cortex (PFC) is a critical non-invasive brain stimulation (NIBS) target for treating depression. However, the alterations of brain activations post-intervention remain inconsistent and the clinical moderators that could improve symptomatic effectiveness are unclear. The study aim was to systematically review the effectiveness of NIBS on depressive symptoms targeting PFC in functional magnetic resonance imaging (fMRI) studies. In our study, we delivered a combined activation likelihood estimation (ALE) meta-analysis and meta-regression. Until November 2020, three databases (PubMed, Web of Science, EMBASE) were searched and 14 studies with a total sample size of 584 were included in the ALE meta-analysis; after NIBS, four clusters in left cerebrum revealed significant activation while two clusters in right cerebrum revealed significant deactivation (P < 0.001, cluster size >150 mm3). Eleven studies were statistically reanalyzed for depressive symptoms pre-post active-NIBS and the pooled effect size was very large [(d = 1.82, 95%CI (1.23, 2.40)]; significant moderators causing substantial heterogeneity (Chi squared = 75.25, P < 0.01; I2 = 87%) were detected through subgroup analysis and univariate meta-regression. Multivariate meta-regression was then conducted accordingly and the model suggested good fitness (Q = 42.32, P < 0.01). In all, NIBS targeting PFC balanced three core depressive-related neurocognitive networks (the salience network, the default mode network, and the central executive network); the striatum played a central role and might serve as a candidate treatment biomarker; gender difference, treatment-resistant condition, comorbidity, treatment duration, and localization all contributed to moderating depressive symptoms during NIBS. More high-quality, multi-center randomized controlled trails delivering personalized NIBS are needed for clinical practice in the future.
Background: The lack of clearly defined neuromodulation targets has contributed to the inconsistent results of real-time fMRI-based neurofeedback (rt-fMRI-NF) for the treatment of chronic pain. Functional neurosurgery (funcSurg) approaches have shown more consistent effects in reducing pain in patients with severe chronic pain.
Objective: This study aims to redefine rt-fMRI-NF targets for chronic pain management informed by funcSurg studies.
Methods: Based on independent systematic reviews, we identified the neuromodulation targets of the rt-fMRI-NF (in acute and chronic pain) and funcSurg (in chronic pain) studies. We then characterized the underlying functional networks using a subsample of the 7 T resting-state fMRI dataset from the Human Connectome Project. Principal component analyses (PCA) were used to identify dominant patterns (accounting for a cumulative explained variance >80%) within the obtained functional maps, and the overlap between these PCA maps and canonical intrinsic brain networks (default, salience, and sensorimotor) was calculated using a null map approach.
Results: The anatomical targets used in rt-fMRI-NF and funcSurg approaches are largely distinct, with the middle cingulate cortex as a common target. Within the investigated canonical rs-fMRI networks, these approaches exhibit both divergent and overlapping functional connectivity patterns. Specifically, rt-fMRI-NF approaches primarily target the default mode network (P value range 0.001-0.002) and the salience network (P = 0.002), whereas funcSurg approaches predominantly target the salience network (P = 0.001) and the sensorimotor network (P value range 0.001-0.023).
Conclusion: Key hubs of the salience and sensorimotor networks may represent promising targets for the therapeutic application of rt-fMRI-NF in chronic pain.
This review examines the methodological challenges and advancements in laminar functional magnetic resonance imaging (fMRI). With the advent of ultra-high-field MRI scanners, laminar fMRI has become pivotal in elucidating the intricate micro-architectures and functionalities of the human brain at a mesoscopic scale. Despite its profound potential, laminar fMRI faces significant challenges such as signal loss at high spatial resolution, limited specificity to laminar signatures, complex layer-specific analysis, the necessity for precise anatomical alignment, and prolonged acquisition times. This review discusses current methodologies, highlights typical challenges in laminar fMRI research, introduces innovative sequence and analysis methods, and outlines potential solutions for overcoming existing technical barriers. It aims to provide a technical overview of the field's current state, emphasizing both the impact of existing hurdles and the advancements that shape future prospects.
Brain network control theory (NCT) is a groundbreaking field in neuroscience that employs system engineering and cybernetics principles to elucidate and manipulate brain dynamics. This review examined the development and applications of NCT over the past decade. We highlighted how NCT has been effectively utilized to model brain dynamics, offering new insights into cognitive control, brain development, the pathophysiology of neurological and psychiatric disorders, and neuromodulation. Additionally, we summarized the practical implementation of NCT using the nctpy package. We also presented the doubts and challenges associated with NCT and efforts made to provide better empirical validations and biological underpinnings. Finally, we outlined future directions for NCT, covering its development and applications.
Background: The hippocampus has been widely reported to be involved in the neuropathology of major depressive disorder (MDD). All the previous researches adopted group-level hippocampus subregions atlas to investigate abnormal functional connectivities in MDD in absence of capturing individual variability. In addition, the molecular basis of functional impairments of hippocampal subregions in MDD remains elusive.
Objective: We aimed to reveal functional disruptions and recovery of individual hippocampal subregions in MDD patients before and after ECT and linked these functional connectivity differences to transcriptomic profiles to reveal molecular mechanism.
Methods: we used group guided individual functional parcellation approach to define individual subregions of hippocampus for each participant. Resting-state functional connectivity (FC) analysis of individual hippocampal subregions was conducted to investigate functional disruptions and recovery in MDD patients before and after ECT. Spatial association between functional connectivity differences and transcriptomic profiles was employed to reveal molecular mechanism.
Results: MDD patients showed increased FCs of the left tail part of hippocampus with dorsolateral prefrontal cortex and middle temporal gyrus while decreased FC with primary visual cortex. These abnormal FCs in MDD patients were normalized after ECT. In addition, we found that functional disruptions of the left tail part of hippocampus in MDD were mainly related to synaptic signaling and transmission, ion transport, cell-cell signaling and neurogenesis.
Conclusion: Our findings provide initial evidence for functional connectome disruption of individual hippocampal subregions and their molecular basis in MDD.