Overview of functional magnetic resonance imaging

The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review

INTRODUCTION

The prevalence of neurodegenerative diseases has significantly increased, necessitating a deeper understanding of their symptoms, diagnostic processes, and prevention strategies. Frontotemporal dementia (FTD) and Alzheimer's disease (AD) are two prominent neurodegenerative conditions that present diagnostic challenges due to overlapping symptoms. To address these challenges, experts utilize a range of imaging techniques, including magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), positron emission tomography (PET), and single-photon emission computed tomography (SPECT). These techniques facilitate a detailed examination of the manifestations of these diseases. Recent research has demonstrated the potential of artificial intelligence (AI) in automating the diagnostic process, generating significant interest in this field.

MATERIALS AND METHODS

This narrative review aims to compile and analyze articles related to the AI-assisted diagnosis of FTD and AD. We reviewed 31 articles published between 2012 and 2024, with 23 focusing on machine learning techniques and 8 on deep learning techniques. The studies utilized features extracted from both single imaging modalities and multi-modal approaches, and evaluated the performance of various classification models.

RESULTS

Among the machine learning studies, Support Vector Machines (SVM) exhibited the most favorable performance in classifying FTD and AD. In deep learning studies, the ResNet convolutional neural network outperformed other networks.

CONCLUSION

This review highlights the utility of different imaging modalities as diagnostic aids in distinguishing between FTD and AD. However, it emphasizes the importance of incorporating clinical examinations and patient symptom evaluations to ensure comprehensive and accurate diagnoses.

Assessing microbiota in vivo: debugging with medical imaging

The microbiota is integral to human health and has been mostly characterized through various ex vivo 'omic'-based approaches. To better understand the real-time function and impact of the microbiota, in vivo molecular imaging is required. With technologies such as positron emission tomography (PET), magnetic resonance imaging (MRI), and computed tomography (CT), insight into microbiological processes may be coupled to in vivo information. Noninvasive imaging enables longitudinal tracking of microbes and their components in real time; mapping of microbiota biodistribution, persistence and migration; and simultaneous monitoring of host physiological responses. The development of molecular imaging for clinical translation is an interdisciplinary science, with broad implications for deeper understanding of host-microbe interactions and the role(s) of the microbiome in health and disease.

A 32-channel high-impedance honeycomb-shaped receive array for temporal lobes exploration at 11.7T

PURPOSE

The newly operational 11.7T Iseult scanner provides an improved global SNR in the human brain. This gain in SNR can be pushed even further locally by designing region-focused dense receive arrays. The temporal lobes are particularly interesting to neuroscientists as they are associated with language and concept recognition. Our main goal was to maximize the SNR in the temporal lobes and provide high-acceleration capabilities for fMRI studies.

METHODS

We designed and developed a 32-channel receive array made of non-overlapped hexagonal loops. The loops were arranged in a honeycomb pattern and targeted the temporal lobes. They were placed on a flexible neoprene cap closely fitting the head. A new stripline design with a high impedance was proposed and applied for the first time at 11.7T. Specific homebuilt miniaturized low-impedance preamplifiers were directly mounted on the loops, providing preamplifier decoupling in a compact and modular design. Using an anatomical phantom, we experimentally compared the SNR and parallel imaging performance of the region-focused cap to a 32-channel whole-brain receive array at 11.7T.

RESULTS

The experimental results showed a 1.7-time higher SNR on average in the temporal lobes compared to the whole brain receive array. The g-factor is also improved when undersampling in the antero-posterior and head-foot directions.

CONCLUSION

A significant SNR boost in the temporal lobes was demonstrated at 11.7T compared to the whole-brain receive array. The parallel imaging capabilities were also improved in the temporal lobes in some acceleration directions.

Aberrant functional connectivity of amygdala subregions in temporal lobe epilepsy with ictal panic

OBJECTIVE

The amygdala joins the model of fear neurocircuitry for its subregional roles in processing and mediating panic. This study aims to explore the underlying neuromechanisms of temporal lobe epilepsy (TLE) patients with ictal panic (IP) by investigating the amygdala subregions functional connectivity (FC) alteration.

METHODS

18 TLE patients with IP (TLE-IP group), 23 TLE patients without IP (TLE-none-IP group) and 22 age- and sex- matched healthy controls (HC) were enrolled and required to take resting-state functional magnetic resonance imaging (rs-fMRI) scanning. The basolateral (BLA), centromedial (CMA), and superficial (SFA) amygdala subregions were extracted from Juelich histological atlas. The amygdala subregions-based FC was computed and compared among three groups.

RESULTS

The TLE-IP group demonstrated stronger FC between the left BLA and right middle frontal gyrus (MFG) than the TLE-none-IP group and HC. Compared with the TLE-none-IP group and HC, the TLE-IP group showed increased FC between the right BLA and right postcentral gyrus. The FC between the left BLA/SFA and the orbital part of right MFG increased in the TLE-IP group. Furthermore, the TLE-IP group exhibited decreased FC between the left CMA and pons. Further analysis indicated altered FC between the amygdala subregions and the pons, precuneus and thalamus in the left-sided TLE-IP group, but the MFG, inferior parietal gyrus, supplementary motor area and cerebellum in the right-sided TLE-IP group.

CONCLUSIONS

The present study revealed aberrant amygdala subregions-based FC in TLE patients with IP. These findings offer unique insights into the understanding of fear neurocircuitry in TLE patients with IP.

Functional MRI and cognition in multiple sclerosis-Where are we now?

Multiple sclerosis-related cognitive impairment (MSrCI) affects most patients with multiple sclerosis (MS), significantly contributing to disability and socioeconomic challenges. MSrCI manifests across all disease stages, mainly impacting working memory, information processing, and attention. To date, the underlying mechanisms of MSrCI remain unclear, with its pathogenesis considered multifactorial. While conventional MRI findings correlate with MSrCI, there is no consensus on reliable imaging metrics to detect or diagnose cognitive impairment (CI). Functional MRI (fMRI) has provided unique insights into the brain's neuroplasticity mechanisms, revealing evidence of compensatory mechanisms in response to tissue damage, both beneficial and maladaptive. This review summarizes the current literature on the application of resting-state fMRI (rs-fMRI) and task-based fMRI (tb-fMRI) in understanding neuroplasticity and its relationship with cognitive changes in people with MS (pwMS). Searches of databases, including PubMed/Medline, Embase, Scopus, and the Web of Science, were conducted for the most recent fMRI cognitive studies in pwMS. Key findings ifrom rs-fMRI studies reveal disruptions in brain connectivity and hub integration, leading to CI due to decreased network efficiency. tb-fMRI studies highlight abnormal brain activation patterns in pwMS, with evidence of increased fMRI activity in earlier disease stages as a beneficial compensatory response, followed by reduced activation correlating with increased lesion burden and cognitive decline as the disease progresses. This suggests a gradual exhaustion of compensatory mechanisms over time. These findings support fMRI not only as a diagnostic tool for MSrCI but also as a potential imaging biomarker to improve our understanding of disease progression.

Potential Neuroimaging Biomarkers for Autism Spectrum Disorder: A Comprehensive Review of MR Imaging, fMR Imaging, and PET Studies

Autism spectrum disorder (ASD) is a characteristically heterogeneous disorder, as multiple neurodevelopmental disorders are characterized by similar symptomology and behavior. Research has shown that individuals with ASD benefit from early intervention; neuroimaging data may reveal information that cannot be obtained from traditional behavioral analysis. This review discusses the use of structural MR imaging, functional MR imaging (fMR imaging), and PET in the detection of ASD. Larger datasets, standardized methods of collection and analysis, and more robust meta-analyses are required to implement the observed biomarkers and improve the lives of patients living with AUD.

Deciphering brain activation during wrist movements: comparative fMRI and fNIRS analysis of active, passive, and imagery states

Understanding the complex activation patterns of brain regions during motor tasks is crucial. Integrated functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) offers advanced insights into how brain activity fluctuates with motor activities. This study explores neuronal activation patterns in the cerebral cortex during active, passive, and imagined wrist movements using these functional imaging techniques. Data were collected from 10 right-handed volunteers performing a motor task using fMRI and fNIRS. fMRI utilized a 3T scanner and a 20-channel head coil, while fNIRS recorded data with a 48-channel device at 765 nm and 855 nm. Analysis focused on key motor and sensory cortices using NIRS-SPM and SPM12, applying a significance threshold of p < 0.05 and a minimum cluster size of 10 voxels for group analysis. Super-threshold voxels were identified with FWE thresholding in SPM12. For activation map extraction we focused on the primary motor cortex, primary somatosensory cortex, somatosensory association cortex, premotor cortex, and supplementary motor cortex. Both fMRI and fNIRS detected activation in the primary motor cortex (M1). The primary somatosensory cortex was found to influence movement direction coding, with smaller activation sizes for upward movements. Combining fNIRS with fMRI provided clearer differentiation of brain activation patterns for wrist movements in various directions and conditions (p < 0.05). This study highlights variations in left motor cortex activity across different movement states. fNIRS proved effective in detecting brain function and showed strong correlation with fMRI results, suggesting it as a viable alternative for those unable to undergo fMRI.

Naturalistic reading of multi-page texts elicits spatially extended modulation of oscillatory activity in the right hemisphere

The study of the cortical basis of reading has greatly benefited from the use of naturalistic paradigms that permit eye movements. However, due to the short stimulus lengths used in most naturalistic reading studies, it remains unclear how reading of texts comprising more than isolated sentences modulates cortical processing. To address this question, we used magnetoencephalography to study the spatiospectral distribution of oscillatory activity during naturalistic reading of multi-page texts. In contrast to previous results, we found abundant activity in the right hemisphere in several frequency bands, whereas reading-related modulation of neural activity in the left hemisphere was quite limited. Our results show that the role of the right hemisphere may be importantly emphasized as the reading process extends beyond single sentences.

Exploring the neurobiological correlates of psilocybin-assisted psychotherapy in eating disorders: a review of potential methodologies and implications for the psychedelic study design

Eating disorders (EDs) are a group of debilitating mental illnesses characterized by maladaptive eating behaviors and severe cognitive-emotional dysfunction, directly affecting 1-3% of the population. Standard treatments are not effective in approximately one third of ED cases, representing the need for scientific advancement. There is emerging evidence for the safety and efficacy of psilocybin-assisted psychotherapy (PAP) to improve treatment outcomes in individuals with EDs. However, the limited knowledge of the neurobiological mechanisms underlying the therapeutic effects of PAP restricts the ability to confirm its clinical utility. This narrative review presents an overview of methodologies used to elucidate the pathophysiological mechanisms of EDs or the effects of psilocybin that could be employed to probe the neurobiological correlates of PAP in EDs, including magnetic resonance imaging and molecular neuroimaging techniques, electrophysiological approaches, and neuroplasticity markers. Finally, the implications of these methodologies are described in relation to the unique features of the psychedelic study design, challenges, limitations, and future directions to advance the field. This paper represents a valuable resource for scientists during study conceptualization and design phases and stimulates advancement in the identification of effective therapeutic interventions for EDs.

A first-in-human application of OPM-MEG for localizing motor activity area: Compared to functional MRI

BACKGROUND

Accurately localizing brain motor areas is vital for protecting motor function during neurosurgical procedures. Magnetoencephalography (MEG) based on optically pumped magnetometer (OPM) improves the availability of MEG in clinical applications. The aim of this study is to evaluate the availability, accuracy and precision of "OPM-MEG" for localizing motor areas in brain tumor patients and healthy individuals.

METHODS

Participants were enrolled and subjected to primary motor area localization by both 3T-fMRI and 128-channel OPM-MEG examinations. The localization accuracy (ability of mapping on the anatomical location) and precision (activation signal centralization) were compared between the two methods, and accuracy was further validated by intraoperative direct cortical electrical stimulation (DCS) on the localized area with assistance of neuro-navigation system.

RESULT

A total of 12 participants (7 brain tumor patients and 5 healthy individuals) were enrolled and all had successful localization for motor areas by both methods. The average time of OPM-MEG examination for each limb function was approximately 9 min. The localizations by both methods mainly covered the anatomical location of primary motor cortex and were partially overlapped. The motor activation signal identified by OPM-MEG was more centralized than fMRI did. The centroid of motor area localized by the OPM-MEG deviated from it by fMRI, with a mean distance of 19.7 mm and 27.48 mm for hand or foot localization, respectively. Furthermore, the OPM-MEG centroid for hand movement successfully triggered corresponding hand response by DCS.

CONCLUSIONS

In this first-in-human study exploring the potential of OPM-MEG in functional localization of motor areas, we revealed its availability and reliability in mapping motor areas, demonstrating it as a promising tool in assisting neurosurgical practice and neuroscience research.

Piezo1 activation on microglial cells exacerbates demyelination in sepsis by influencing the CCL25/GRP78 pathway

BACKGROUND

In sepsis-associated encephalopathy (SAE), the activation of microglial cells and ensuing neuroinflammation are important in the underlying pathological mechanisms. Increasing evidence suggests that the protein Piezo1 functions as a significant regulator of neuroinflammation. However, the influence of Piezo1 on microglial cells in the context of SAE has not yet been determined. This study aims to investigate the role of Piezo1 in microglial cells in the context of SAE.

METHODS

By inducing cecal ligation and puncture (CLP), a mouse model of SAE was established, while the control group underwent a sham surgery in which the cecum was exposed without ligation and puncture. Piezo1 knockout mice were employed in this study. Morris water maze tests were conducted between Days 14 and 18 postop to assess both the motor activity and cognitive function. A proteomic analysis was conducted to assess the SAE-related pathways, whereas a Mendelian randomization analysis was conducted to identify the pathways associated with cognitive impairment. Dual-label immunofluorescence and flow cytometry were used to assess the secretion of inflammatory factors, microglial status, and oligodendrocyte development. Electron microscopy was used to evaluate axonal myelination. A western blot analysis was conducted to evaluate the influence of Piezo1 on oligodendrocyte ferroptosis.

RESULTS

The results of the bioinformatics analysis have revealed the significant involvement of CCL25 in the onset and progression of SAE-induced cognitive impairment. SAE leads to cognitive dysfunction by activating the microglial cells. The release of CCL25 by the activated microglia initiates the demyelination of oligodendrocytes in the hippocampus, resulting in ferroptosis and the disruption of hippocampal functional connectivity. Of note, the genetic knockout of the Piezo1 gene mitigates these changes. The treatment with siRNA targeting Piezo1 effectively reduces the secretion of inflammatory mediators CCL25 and IL-18 by inhibiting the p38 pathway, thus preventing the ferroptosis of oligodendrocytes through the modulation of the CCL25/GPR78 axis.

CONCLUSION

Piezo1 is involved in the activation of microglia and demyelinating oligodendrocytes in the animal models of SAE, resulting in cognitive impairment. Consequently, targeting Piezo1 suppression can be a promising approach for therapeutic interventions aimed at addressing cognitive dysfunction associated with SAE.

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning

OBJECTIVE

Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated.

METHODS

In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs).

RESULTS

RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786.

CONCLUSION

This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.

Forecasting fMRI images from video sequences: linear model analysis

Over the past few decades, a variety of significant scientific breakthroughs have been achieved in the fields of brain encoding and decoding using the functional magnetic resonance imaging (fMRI). Many studies have been conducted on the topic of human brain reaction to visual stimuli. However, the relationship between fMRI images and video sequences viewed by humans remains complex and is often studied using large transformer models. In this paper, we investigate the correlation between videos presented to participants during an experiment and the resulting fMRI images. To achieve this, we propose a method for creating a linear model that predicts changes in fMRI signals based on video sequence images. A linear model is constructed for each individual voxel in the fMRI image, assuming that the image sequence follows a Markov property. Through the comprehensive qualitative experiments, we demonstrate the relationship between the two time series. We hope that our findings contribute to a deeper understanding of the human brain's reaction to external stimuli and provide a basis for future research in this area.

Comparison of MRI head motion indicators in 40,969 subjects informs neuroimaging study design

Head motion during MRI compromises image quality for clinical assessments and research. Active motion reduction strategies are effective but rarely applied due to uncertainty in their value for a given study. The ability to anticipate motion based on group characteristics would aid effective neuroimaging study design. This study compared putative motion indicators for their association to fMRI head motion in a large UK Biobank cohort (n = 40,969, aged 54.9 ± 7.5 years, 53% male). Body Mass Index (BMI; βadj = .050, p < .001) and ethnicity (βadj = 0.068, p < 0.001) were the strongest indicators of head motion. A ten-point increase in BMI, which is the difference between "healthy" and "obese", corresponded to a 51% increase in motion. Findings were similar in a subgroup with no lifetime diagnoses (n = 6858). Motion was not significantly increased in individuals with psychiatric disorders, musculoskeletal disorders, or diabetes. The hypertension subgroup exhibited significantly increased motion (p = 0.048). Cognitive task performance (t = 110.83, p < 0.001) and prior scan experience (t = 7.16, p < 0.001) were associated with increased head motion. Our results inform decision making for implementation of motion reduction strategies in MRI. BMI outweighs other motion indicators, while blood pressure, age, smoking and caffeine consumption are relatively less influential. Disease diagnosis alone is not a good indicator of MRI head motion.

Decoding Schizophrenia: How AI-Enhanced fMRI Unlocks New Pathways for Precision Psychiatry

Schizophrenia, a highly complex psychiatric disorder, presents significant challenges in diagnosis and treatment due to its multifaceted neurobiological underpinnings. Recent advancements in functional magnetic resonance imaging (fMRI) and artificial intelligence (AI) have revolutionized the understanding and management of this condition. This manuscript explores how the integration of these technologies has unveiled key insights into schizophrenia's structural and functional neural anomalies. fMRI research highlights disruptions in crucial brain regions like the prefrontal cortex and hippocampus, alongside impaired connectivity within networks such as the default mode network (DMN). These alterations correlate with the cognitive deficits and emotional dysregulation characteristic of schizophrenia. AI techniques, including machine learning (ML) and deep learning (DL), have enhanced the detection and analysis of these complex patterns, surpassing traditional methods in precision. Algorithms such as support vector machines (SVMs) and Vision Transformers (ViTs) have proven particularly effective in identifying biomarkers and aiding early diagnosis. Despite these advancements, challenges such as variability in methodologies and the disorder's heterogeneity persist, necessitating large-scale, collaborative studies for clinical translation. Moreover, ethical considerations surrounding data integrity, algorithmic transparency, and patient individuality must guide AI's integration into psychiatry. Looking ahead, AI-augmented fMRI holds promise for tailoring personalized interventions, addressing unique neural dysfunctions, and improving therapeutic outcomes for individuals with schizophrenia. This convergence of neuroimaging and computational innovation heralds a transformative era in precision psychiatry.

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging

INTRODUCTION

Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of blood oxygen level-dependent (BOLD) signals; however, a few studies have investigated BOLD signal stability.

METHODS

We conducted a resting-state functional study in 42 patients with MDD and 42 healthy controls (HC) matched for age and sex. This included the BOLD signal stability, resting-state functional connectivity (RSFC) analysis, correlation analysis, and support vector machine (SVM) analysis.

RESULTS

The BOLD signal stability of the left fusiform gyrus, right inferior temporal gyrus, right temporal pole superior temporal gyrus, and left thalamus was significantly lower in the MDD group compared to the HC group. Further RSFC analysis revealed that the connectivity between right inferior temporal gyrus and both left inferior temporal gyrus and left supramarginal gyrus was significantly reduced in the MDD group. Additionally, the RSFC levels of left thalamus and right thalamus were decreased. Combining BOLD signal stability and RSFC, the SVM-based classification model achieved an accuracy of 80.95% (sensitivity: 78.57%; specificity: 83.33%; receiver-operating characteristic area under the curve: 0.8793).

CONCLUSION

The integration of the BOLD signal stability index and RSFC index demonstrates a robust capability to differentiate between individuals with MDD and HC subjects. We tentatively believe that a combination of the BOLD signal stability index and RSFC can be used to diagnose MDD.

Optimizing infant neuroimaging methods to understand the neurodevelopmental impacts of early nutrition and feeding

There is strong evidence proper nutrition is imperative for healthy infant neurodevelopment, providing the neural foundations for later cognition and behavior. Over the first years of life infants are supported by unique sources of nutrition (e.g., human milk, alternative milk sources). It is during this time that the brain undergoes its most drastic changes during postnatal development. Past research has examined associations between infant feeding and nutrition and morphological features of the brain, yet there remains a paucity of information on functional characteristics of neural activity during feeding. Within this article, we discuss how neuroimaging modalities can be optimized for researching the impacts of infant feeding and nutrition on brain function. We review past research utilizing EEG and fNIRS and describe our efforts to further develop neuroimaging approaches that allow for measurement of brain activity during active feeding with greater spatial resolution (e.g., fMRI and OPM-MEG). We also discuss current challenges, as well as the scientific and logistical limitations of each method. Once protocols have been optimized, these methods will provide the requisite insight into the underlying mechanisms of nutritional and feeding impacts on neurodevelopment, providing the missing piece in the field's efforts to understand this essential and ubiquitous part of early life.

Association of bidirectional network cores in the brain with perceptual awareness and cognition

The brain comprises a complex network of interacting regions. To understand the roles and mechanisms of this intricate network, it is crucial to elucidate its structural features related to cognitive functions. Recent empirical evidence suggests that both feedforward and feedback signals are necessary for conscious perception, emphasizing the importance of subnetworks with bidirectional interactions. However, the link between such subnetworks and conscious perception remains unclear due to the complexity of brain networks. In this study, we propose a framework for extracting subnetworks with strong bidirectional interactions-termed the "cores" of a network-from brain activity. We applied this framework to resting-state and task-based human fMRI data from participants of both sexes to identify regions forming strongly bidirectional cores. We then explored the association of these cores with conscious perception and cognitive functions. We found that the extracted central cores predominantly included cerebral cortical regions rather than subcortical regions. Additionally, regarding their relation to conscious perception, we demonstrated that the cores were composed of regions previously reported to be affected by electrical stimulation that altered conscious perception. Furthermore, in relation to cognitive functions, based on a meta-analysis and comparison of the core structure with a cortical functional connectivity gradient, we found that the central cores were related to unimodal sensorimotor functions. The proposed framework provides novel insights into the roles of network cores with strong bidirectional interactions in conscious perception and unimodal sensorimotor functions.

Multifunctional Neural Probes Enable Bidirectional Electrical, Optical, and Chemical Recording and Stimulation In Vivo

Recording and modulation of neuronal activity enables the study of brain function in health and disease. While translational neuroscience relies on electrical recording and modulation techniques, mechanistic studies in rodent models leverage genetic precision of optical methods, such as optogenetics and fluorescent indicator imaging. In addition to electrical signal transduction, neurons produce and receive diverse chemical signals which motivate tools to probe and modulate neurochemistry. Although the past decade has delivered a wealth of technologies for electrophysiology, optogenetics, chemical sensing, and optical recording, combining these modalities within a single platform remains challenging. This work leverages materials selection and convergence fiber drawing to permit neural recording, electrical stimulation, optogenetics, fiber photometry, drug and gene delivery, and voltammetric recording of neurotransmitters within individual fibers. Composed of polymers and non-magnetic carbon-based conductors, these fibers are compatible with magnetic resonance imaging, enabling concurrent stimulation and whole-brain monitoring. Their utility is demonstrated in studies of the mesolimbic reward pathway by interfacing with the ventral tegmental area and nucleus accumbens in mice and characterizing the neurophysiological effects of a stimulant drug. This study highlights the potential of these fibers to probe electrical, optical, and chemical signaling across multiple brain regions in both mechanistic and translational studies.

Transformation but not generation of motor images is disrupted following stimulation over the left inferior parietal lobe

Motor imagery (MI) involves the generation, maintenance, and transformation of motor images; yet, the neural underpinnings of each stage are not well understood. Here, we investigated the role of the left inferior parietal lobe (IPL) in the stages of MI. Healthy participants (N = 20) engaged in a MI task (making judgments about hands presented on a screen; hand laterality judgment task) over two days. Past literature demonstrates the mental rotation of hands in this task involves implicit MI (i.e., where MI occurs spontaneously in the absence of explicit instructions). During the task, active (Day A; 120% resting motor threshold) or sham (Day B; placebo) neuronavigated transcranial magnetic stimulation (TMS) was applied to the left IPL (location determined from past neuroimaging work) on 50% of trials at 250, 500, or 750ms post-stimulus onset, corresponding to different stages of MI. A/B days were randomized across participants. Linear mixed effects (LME) modelling conducted on reaction time and accuracy revealed that longer reaction times were observed when TMS was delivered at 750ms after trial onset, and more greatly for active vs. sham stimulation. This effect was exacerbated for palm-vs. back-view stimuli and for left vs. right hands. Accuracy overall was decreased for active vs. sham stimulation, and to a greater extent for palm-vs. back-view stimuli. Findings suggest that the left IPL is involved in image transformation. Overall this work informs on the neural underpinnings of the stages of MI.

Cerebral hyperactivation across the Alzheimer's disease pathological cascade

Neuronal dysfunction in specific brain regions or across distributed brain networks is a known feature of Alzheimer's disease. An often reported finding in the early stage of the disease is the presence of increased functional MRI (fMRI) blood oxygenation level-dependent signal under task conditions relative to cognitively normal controls, a phenomenon known as 'hyperactivation'. However, research in the past decades yielded complex, sometimes conflicting results. The magnitude and topology of fMRI hyperactivation patterns have been found to vary across the preclinical and clinical spectrum of Alzheimer's disease, including concomitant 'hypoactivation' in some cases. These incongruences are likely due to a range of factors, including the disease stage at which the cohort is examined, the brain areas or networks studied and the fMRI paradigm utilized to evoke these functional abnormalities. Additionally, a perennial question pertains to the nature of hyperactivation in the context of Alzheimer's disease. Some propose it reflects compensatory mechanisms to sustain cognitive performance, while others suggest it is linked to the pathological disruption of a highly regulated homeostatic cycle that contributes to, or even drives, disease progression. Providing a coherent narrative for these empirical and conceptual discrepancies is paramount to develop disease models, understand the synergy between hyperactivation and the Alzheimer's disease pathological cascade and tailor effective interventions. We first provide a comprehensive overview of functional brain changes spanning the course from normal ageing to the clinical spectrum of Alzheimer's disease. We then highlight evidence supporting a close relationship between fMRI hyperactivation and in vivo markers of Alzheimer's pathology. We primarily focus on task-based fMRI studies in humans, but also consider studies using different functional imaging techniques and animal models. We then discuss the potential mechanisms underlying hyperactivation in the context of Alzheimer's disease and provide a testable framework bridging hyperactivation, ageing, cognition and the Alzheimer's disease pathological cascade. We conclude with a discussion of future challenges and opportunities to advance our understanding of the fundamental disease mechanisms of Alzheimer's disease, and the promising development of therapeutic interventions incorporating or aimed at hyperactivation and large-scale functional systems.

Impaired brain glucose metabolism in glucagon-like peptide-1 receptor knockout mice

BACKGROUND

Quantitative mapping of the brain's metabolism is a critical tool in studying and diagnosing many conditions, from obesity to neurodegenerative diseases. In particular, noninvasive approaches are urgently required. Recently, there have been promising drug development approaches for the treatment of disorders related to glucose metabolism in the brain and, therefore, against obesity-associated diseases. One of the most important drug targets to emerge has been the Glucagon-like peptide-1 (GLP-1) and its receptor (GLP-1R). GLP and GLP-1R play an important role in regulating blood sugar and maintaining energy homeostasis. However, the macroscopic effects on brain metabolism and function due to the presence of GLP-1R are unclear.

METHODS

To explore the physiological role of GLP-1R in mouse brain glucose metabolism, and its relationship to brain function, we used three methods. We used deuterium magnetic resonance spectroscopy (DMRS) to provide quantitative information about metabolic flux, fluorodeoxyglucose positron emission tomography (FDG-PET) to measure brain glucose metabolism, and resting state-functional MRI (rs-fMRI) to measure brain functional connectivity. We used these methods in both mice with complete GLP-1R knockout (GLP-1R KO) and wild-type C57BL/6N (WT) mice.

RESULTS

The metabolic rate of GLP-1R KO mice was significantly slower than that of WT mice (p = 0.0345, WT mice 0.02335 ± 0.057 mM/min, GLP-1R KO mice 0.01998 ± 0.07 mM/min). Quantification of the mean [18F]FDG signal in the whole brain also showed significantly reduced glucose uptake in GLP-1R KO mice versus control mice (p = 0.0314). Observing rs-fMRI, the functional brain connectivity in GLP-1R KO mice was significantly lower than that in the WT group (p = 0.0032 for gFCD, p = 0.0002 for whole-brain correlation, p < 0.0001 for ALFF).

CONCLUSIONS

GLP-1R KO mice exhibit impaired brain glucose metabolism to high doses of exogenous glucose, and they also have reduced functional connectivity. This suggests that the GLP-1R KO mouse model may serve as a model for correlated metabolic and functional connectivity loss.

Topological cluster statistic (TCS): Toward structural connectivity-guided fMRI cluster enhancement

Functional magnetic resonance imaging (fMRI) studies most commonly use cluster-based inference to detect local changes in brain activity. Insufficient statistical power and disproportionate false-positive rates reportedly hinder optimal inference. We propose a structural connectivity-guided clustering framework, called topological cluster statistic (TCS), that enhances sensitivity by leveraging white matter anatomical connectivity information. TCS harnesses multimodal information from diffusion tractography and functional imaging to improve task fMRI activation inference. Compared to conventional approaches, TCS consistently improves power over a wide range of effects. This improvement results in a 10%-50% increase in local sensitivity with the greatest gains for medium-sized effects. TCS additionally enables inspection of underlying anatomical networks and thus uncovers knowledge regarding the anatomical underpinnings of brain activation. This novel approach is made available in the PALM software to facilitate usability. Given the increasing recognition that activation reflects widespread, coordinated processes, TCS provides a way to integrate the known structure underlying widespread activations into neuroimaging analyses moving forward.

The Effect of Efavirenz on Reward Processing in Asymptomatic People Living with HIV: A Randomized Controlled Trial

Functional magnetic resonance imaging (fMRI) studies have demonstrated that HIV-infection affects the fronto-striatal network. It has not been examined what impact efavirenz (EFV), an antiretroviral drug notorious for its neurocognitive effects, has on the reward system: a key subcomponent involved in depressive and apathy symptoms. Therefore, this study aims to investigate the effect of EFV on reward processing using a monetary incentive delay (MID) task. In this multicenter randomized controlled trial, asymptomatic adult participants stable on emtricitabine/tenofovirdisoproxil fumarate (FTC/TDF)/EFV were randomly assigned in a 2:1 ratio to switch to FTC/TDF/rilpivirine (RPV) (n = 30) or continue taking FTC/TDF/EFV (n = 13). At baseline and 12 weeks after therapy switch, both groups performed an MID task. Behavior and functional brain activity related to reward anticipation and reward outcome were assessed with blood-oxygen-level-dependent fMRI. Both groups were matched for age, education level, and time since HIV diagnosis and on EFV. At the behavioral level, both groups had faster response times and better response accuracy during rewarding versus nonrewarding trials, with no improvement resulting from switching FTC/TDF/EFV to FTC/TDF/RPV. No significant change in activation related to reward anticipation in the ventral striatum was found after switching therapy. Both groups had significantly higher activation levels over time, consistent with a potential learning effect. Similar activity related to reward outcome in the orbitofrontal cortex was found. Discontinuing FTC/TDF/EFV was not found to improve activity related to reward anticipation in asymptomatic people living with HIV, with similar cortical functioning during reward outcome processing. It is therefore likely that EFV does not affect motivational control. Further research is needed to determine whether EFV affects motivational control in HIV populations with different characteristics.

Abnormal temporal variability of thalamo-cortical circuit in patients with moderate-to-severe obstructive sleep apnea

This study investigated the abnormal dynamic functional connectivity (dFC) variability of the thalamo-cortical circuit in patients with obstructive sleep apnea (OSA) and explored the relationship between these changes and the clinical characteristics of patients with OSA. A total of 91 newly diagnosed patients with moderate-to-severe OSA and 84 education-matched healthy controls (HCs) were included. All participants underwent neuropsychological testing and a functional magnetic resonance imaging scan. We explored the thalamo-cortical dFC changes by dividing the thalamus into 16 subregions and combining them using a sliding-window approach. Correlation analysis assessed the relationship between dFC variability and clinical features, and the support vector machine method was used for classification. The OSA group exhibited increased dFC variability between the thalamic subregions and extensive cortical areas, compared with the HCs group. Decreased dFC variability was observed in some frontal-occipital-temporal cortical regions. These dFC changes positively correlated with daytime sleepiness, disease severity, and cognitive scores. Altered dFC variability contributed to the discrimination between patients with OSA and HCs, with a classification accuracy of 77.8%. Our findings show thalamo-cortical overactivation and disconnection in patients with OSA, disrupting information flow within the brain networks. These results enhance understanding of the temporal variability of thalamo-cortical circuits in patients with OSA.

Functional Magnetic Resonance Imaging Analysis of the Clinical Effect and Cerebral Mechanism of Tuina in Lumbar Disc Herniation: Protocol for a Randomized Controlled Parallel Group Trial

BACKGROUND

Lumbar disc herniation (LDH) has become a serious public health and socioeconomic problem. Tuina is a Chinese medicine treatment method based on meridian acupuncture theory and modern anatomy. Tuina can relieve pain and muscle tension and improve functional disorders; this massage is performed by pressing, kneading, pushing, pulling, and shaking the skin, muscles, and bones. However, the mechanism of action and the effect of Tuina as an external treatment on the activities of the central nervous system to relieve LDH pain is unclear. Therefore, we performed functional magnetic resonance imaging (fMRI), which is widely used in pain-related research, as it can detect the effects of different types of pain on brain activity.

OBJECTIVE

Our randomized controlled parallel-group trial aims to compare the effects of Tuina with those of transcutaneous electrical nerve stimulation (TENS) with traction in patients with LDH.

METHODS

This trial will be conducted between May 2024 and April 2025 in the Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine. Seventy-six participants with LDH will be enrolled for this trial and randomly assigned to 2 groups: Tuina intervention group and TENS with traction intervention group. Participants in both groups will receive treatment for 14 days. fMRI will be performed for the main pain measurements by assessing the effect of the intervention on brain activity before and after the end of the intervention. Short-Form McGill Pain Questionnaire, pressure pain thresholds, and the Oswestry disability index will be used to reflect the degree of pain and lumbar dysfunction, and the results will be used as secondary outcome measurements.

RESULTS

The study protocol has been approved by the ethics review committee of The Rehabilitation Hospital affiliated to Fujian University of Traditional Chinese Medicine. This study was registered on May 1, 2024, with the Chinese Clinical Trial Registry. Data collection began on May 2024 and is expected to end on April 2025. Currently, data from this trial are in the collection phase, and no data analysis has been performed. As of July 1, 2024, we have collected data from 21 patients. The results of this trial are expected to be submitted for publication in September 2025.

CONCLUSIONS

This clinical trial will compare the effectiveness of Tuina with that of TENS with traction in the treatment of patients with LDH and will show the cerebral mechanism of Tuina in LDH treatment by using fMRI. The results of our trial will be helpful in clarifying the cerebral mechanism of Tuina in the treatment of LDH and provide a solid foundation for Tuina therapy research.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR2400083784; https://www.chictr.org.cn/showproj.html?proj=225157.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/63852.

Altered spontaneous brain activity patterns in hypertensive retinopathy using fractional amplitude of low-frequency fluctuations: a functional magnetic resonance imaging study

AIM

To study functional brain abnormalities in patients with hypertensive retinopathy (HR) and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations (fALFFs) method.

METHODS

Twenty HR patients and 20 healthy controls (HCs) were respectively recruited. The age, gender, and educational background characteristics of the two groups were similar. After functional magnetic resonance imaging (fMRI) scanning, the subjects' spontaneous brain activity was evaluated with the fALFF method. Receiver operating characteristic (ROC) curve analysis was used to classify the data. Further, we used Pearson's correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR.

RESULTS

The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus (RO-MFG) and right lingual gyrus. In contrast, the values of fALFFs in the left middle temporal gyrus (MTG), left superior temporal pole (STP), left middle frontal gyrus (MFG), left superior marginal gyrus (SMG), left superior parietal lobule (SPL), and right supplementary motor area (SMA) were higher in the HR group. The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group (P<0.001). The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores (r=0.9232; P<0.0001) and depression scores (r=0.9682; P<0.0001).

CONCLUSION

fALFF values in multiple brain regions of HR patients are abnormal, suggesting that these brain regions in HR patients may be dysfunctional, which may help to reveal the pathophysiological mechanisms of HR.

No evidence of altered language laterality in people who stutter across different brain imaging studies of speech and language

A long-standing neurobiological explanation of stuttering is the incomplete cerebral dominance theory, which refers to competition between two hemispheres for 'dominance' over handedness and speech, causing altered language lateralization. Renewed interest in these ideas came from brain imaging findings in people who stutter of increased activity in the right hemisphere during speech production or of shifts in activity from right to left when fluency increased. Here, we revisited this theory using functional MRI data from children and adults who stutter, and typically fluent speakers (119 participants in total) during four different speech and language tasks: overt sentence reading, overt picture description, covert sentence reading and covert auditory naming. Laterality indices were calculated for the frontal and temporal lobes using the laterality index toolbox running in Statistical Parametric Mapping. We also repeated the analyses with more specific language regions, namely the pars opercularis (Brodmann area 44) and pars triangularis (Brodmann area 45). Laterality indices in people who stutter and typically fluent speakers did not differ, and Bayesian analyses provided moderate to anecdotal levels of support for the null hypothesis (i.e. no differences in laterality in people who stutter compared with typically fluent speakers). The proportions of the people who stutter and typically fluent speakers who were left lateralized or had atypical rightward or bilateral lateralization did not differ. We found no support for the theory that language laterality is reduced or differs in people who stutter compared with typically fluent speakers.

Contracted functional connectivity profiles in autism

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.

METHODS

We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.

RESULTS

Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.

LIMITATIONS

Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.

CONCLUSIONS

Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Label-free functional imaging of vagus nerve stimulation-evoked potentials at the cortical surface

Vagus nerve stimulation (VNS) is an FDA-approved stimulation therapy to treat patients with refractory epilepsy. In this work, we use a coherent holographic imaging system to characterize vagus nerve-evoked potentials (VEPs) in the cortex in response to VNS stimulation paradigms without electrode placement or any genetic, structural, or functional labels. We analyze stimulation amplitude up to saturation, pulse width up to 800 μs, and frequency from 10 Hz to 30 Hz, finding that stimulation amplitude strongly modulates VEPs response magnitude (effect size 0.401), while pulse width has a moderate modulatory effect (effect size 0.127) and frequency has almost no modulatory effect (effect size 0.009) on the evoked potential magnitude. We find mild interactions between pulse width and frequency. This non-contact label-free functional imaging technique may serve as a non-invasive rapid-feedback tool to characterize VEPs and may increase the efficacy of VNS in patients with refractory epilepsy.

The neural effects of acupuncture for stroke: a protocol for systematic review and meta-analysis based on fMRI

Background

Stroke is one of the most common causes of death and is the main cause of persistent and acquired disability in adults worldwide. Acupuncture is recommended by the World Health Organization as an alternative and complementary strategy for stroke treatment. However, the mechanism of the neural effects of acupuncture on stroke is still lacking a uniform conclusion. The purpose of this study is to clarify the neural effects of acupuncture for stroke from the perspective of neuroimaging.

Methods

Seven databases, including PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, VIP Database, Wan-Fang Data, and Chinese Biomedical Database, will be searched comprehensively according to the search strategy. All of them will be retrieved from the date of database establishment to December 31, 2023. All randomized controlled trials and observational studies will be considered for inclusion. The risk of bias will be assessed by the Cochrane Risk of Bias tool. Functional imaging of the whole brain in the resting functional state will be the primary outcome. A meta-analysis of primary outcome will be performed using Seed-based D Mapping with Permutation of Subject Images software. The data will be synthesized using a random effects model. Meta-analysis of other clinical data will be performed using RevMan 5.3 software. Publication bias will be assessed through funnel plots. Reports will adhere to the Preferred Reporting Items for Systematic Review and Meta-Analysis.

Conclusion

This study will aggregate the results of independent studies to provide overall evidence for the neural effects of acupuncture for stroke.

Clinical Trial Registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=500306, Identifier CRD42024500306.

Sex differences in functional and structural alterations of hippocampus region in chronic pain: a DTI and resting-state fMRI study

Introduction

It is well known that there are significant differences in the prevalence of chronic pain between males and females. Human and animal imaging studies have shown that chronic pain profoundly alters the structure and function of brain regions. However, there is limited research on the sex-specific mechanisms underlying the brain plasticity and adaptive changes associated with chronic pain. In this article, we conducted a multimodal study to evaluate how nerve injury-induced chronic pain affects the brain.

Methods

Male and female Sprague-Dawley (SD) rats with spared nerve injury (SNI) model underwent resting-state functional magnetic resonance imaging (rs-fMRI) (male sham group: n = 18; male SNI group: n = 18; female sham group: n = 20; female SNI group: n = 18) and magnetic resonance diffusion tensor imaging (DTI) (male sham group: n = 23; male SNI group: n = 21; female sham group: n = 20; female SNI group: n = 21) scanning. ICA method, Fractional amplitude of low-frequency fluctuations (fALFF), immunofluorescence staining, and graph theory analysis was utilized to extract the rs-fMRI changes of brain regions of each group.

Results

Using SNI model, which promotes long-lasting mechanical allodynia, we found that neuropathic pain deeply modified the intrinsic organization of the brain functional network in male and female rats (main effect of operation: F = 298.449, P < 0.001). 64 independent components (ICs) in the brain were divided and assigned to 16 systems. In male rats, we observed significant alterations in the microstructure of the hippocampal cornu ammonis 1 and cornu ammonis 2 (CA1/CA2) region, as indicated by increased mean diffusivity (MD) (CA1_L: P = 0.02; CA1_R: P = 0.031; CA2_L: P = 0.035; CA2_R: P = 0.015) and radial diffusivity (RD) (CA1_L: P = 0.028; CA1_R: P = 0.033; CA2_L: P = 0.037; CA2_R: P = 0.038) values, along with enhanced activating transcription factor 3 (ATF3) expression. Conversely, in female rats, we found significant increases in the fractional amplitude of low frequency fluctuations (fALFF) value within the hippocampal dentate gyrus (DG) (F = 5.419, P = 0.023), accompanied by elevated c-Fos signal (F = 6.269, P = 0.031). Furthermore, graph theory analysis revealed notable differences in the small-world network of the hippocampal system in female rats, characterized by reduced small-world attributes and increased inter-nodal transmission efficiency.

Discussion

Our study indicates sex differences in structural and functional alterations in the hippocampal system in rats under chronic pain conditions. The results suggest that the hippocampus system plays an important role in the different mechanisms of chronic pain in different sexes. These findings provide reliable insights to explore the complex mechanisms underlying sex differences in chronic pain.

Mitigation of Fetal Radiation Injury from Mid-Gestation Total-body Irradiation by Maternal Administration of Mitochondrial-Targeted GS-Nitroxide JP4-039

Victims of a radiation terrorist event will include pregnant women and unborn fetuses. Mitochondrial dysfunction and oxidative stress are key pathogenic factors of fetal radiation injury. The goal of this preclinical study is to investigate the efficacy of mitigating fetal radiation injury by maternal administration of the mitochondrial-targeted gramicidin S (GS)-nitroxide radiation mitigator JP4-039. Pregnant female C57BL/6NTac mice received 3 Gy total-body irradiation (TBI) at mid-gestation embryonic day 13.5 (E13.5). Using novel time-and-motion-resolved 4D in utero magnetic resonance imaging (4D-uMRI), we found TBI caused extensive injury to the fetal brain that included cerebral hemorrhage, loss of cerebral tissue, and hydrocephalus with excessive accumulation of cerebrospinal fluid (CSF). Histopathology of the fetal mouse brain showed broken cerebral vessels and elevated apoptosis. Further use of novel 4D Oxy-wavelet MRI capable of probing in vivo mitochondrial function in intact brain revealed a significant reduction of mitochondrial function in the fetal brain after 3 Gy TBI. This was validated by ex vivo Oroboros mitochondrial respirometry. One day after TBI (E14.5) maternal administration of JP4-039, which passes through the placenta, significantly reduced fetal brain radiation injury and improved fetal brain mitochondrial respiration. Treatment also preserved cerebral brain tissue integrity and reduced cerebral hemorrhage and cell death. JP4-039 administration following irradiation resulted in increased survival of pups. These findings indicate that JP4-039 can be deployed as a safe and effective mitigator of fetal radiation injury from mid-gestational in utero ionizing radiation exposure.

From Cats to the Cortex: Unravelling the Hierarchical Processing System of Vision and Brain Plasticity

The groundbreaking research conducted by neurophysiologists David Hubel and Torsten Wiesel during the late 1950s and 1960s revolutionized the field of visual neuroscience. Through single-unit recordings in the visual cortex of cats, they made several key discoveries that fundamentally changed our understanding of visual processing. Their work introduced the concept of orientation selectivity, revealing that neurons in the visual cortex are specifically tuned to line orientations, thereby illustrating how the brain constructs visual representations through edge detection. Additionally, they discovered ocular dominance columns, the specialized cortical regions that respond preferentially to input from one eye, providing crucial insights into the organization of visual processing and the importance of binocular vision. Hubel and Wiesel's research also established the concept of a critical period in visual development, demonstrating that early visual experiences are essential for the proper maturation of the visual system. This discovery has had significant implications for understanding neural plasticity and the role of sensory input in neural development. The impact of their work goes beyond theoretical knowledge, contributing to the development of therapeutic strategies for some visual disorders and guiding current research into brain plasticity and visual processing. This review synthesizes the monumental contributions of Hubel and Wiesel, evaluating how their key discoveries have shaped subsequent research in visual neuroscience. It traces the evolution of knowledge related to visual pathways, feature detection, and brain plasticity, highlighting the enduring influence of their foundational work on contemporary studies. By exploring the progression from their pioneering findings to modern advancements, this review emphasizes the legacy of Hubel and Wiesel's contributions to our understanding of vision and neural function.

Advances in Imaging for Metastatic Epidural Spinal Cord Compression: A Comprehensive Review of Detection, Diagnosis, and Treatment Planning

Metastatic epidural spinal cord compression (MESCC) is a critical oncologic emergency caused by the invasion of metastatic tumors into the spinal epidural space, leading to compression of the spinal cord. If not promptly diagnosed and treated, MESCC can result in irreversible neurological deficits, including paralysis, significantly impacting the patient's quality of life. Early detection and timely intervention are crucial to prevent permanent damage. Imaging modalities play a pivotal role in the diagnosis, assessment of disease extent, and treatment planning for MESCC. Magnetic resonance imaging (MRI) is the current gold standard due to its superior ability to visualize the spinal cord, epidural space, and metastatic lesions. However, recent advances in imaging technologies have enhanced the detection and management of MESCC. Innovations such as functional MRI, diffusion-weighted imaging (DWI), and hybrid techniques like positron emission tomography-computed tomography (PET-CT) and PET-MRI have improved the accuracy of diagnosis, particularly in detecting early metastatic changes and guiding therapeutic interventions. This review provides a comprehensive analysis of the evolution of imaging techniques for MESCC, focusing on their roles in detection, diagnosis, and treatment planning. It also discusses the impact of these advances on clinical outcomes and future research directions in imaging modalities for MESCC. Understanding these advancements is critical for optimizing the management of MESCC and improving patient prognosis.

Reconstructing Spatiotemporal Trajectories of Visual Object Memories in the Human Brain

How the human brain reconstructs, step-by-step, the core elements of past experiences is still unclear. Here, we map the spatiotemporal trajectories along which visual object memories are reconstructed during associative recall. Specifically, we inquire whether retrieval reinstates feature representations in a copy-like but reversed direction with respect to the initial perceptual experience, or alternatively, this reconstruction involves format transformations and regions beyond initial perception. Participants from two cohorts studied new associations between verbs and randomly paired object images, and subsequently recalled the objects when presented with the corresponding verb cue. We first analyze multivariate fMRI patterns to map where in the brain high- and low-level object features can be decoded during perception and retrieval, showing that retrieval is dominated by conceptual features, represented in comparatively late visual and parietal areas. A separately acquired EEG dataset is then used to track the temporal evolution of the reactivated patterns using similarity-based EEG-fMRI fusion. This fusion suggests that memory reconstruction proceeds from anterior frontotemporal to posterior occipital and parietal regions, in line with a conceptual-to-perceptual gradient but only partly following the same trajectories as during perception. Specifically, a linear regression statistically confirms that the sequential activation of ventral visual stream regions is reversed between image perception and retrieval. The fusion analysis also suggests an information relay to frontoparietal areas late during retrieval. Together, the results shed light onto the temporal dynamics of memory recall and the transformations that the information undergoes between the initial experience and its later reconstruction from memory.

Exercise-induced calf muscle hyperemia quantified with dynamic blood oxygen level-dependent (BOLD) imaging

Muscle hyperemia in exercise is usually the combined result of increased cardiac output and local muscle vasodilation, with the latter reflecting muscle's capacity for increased blood perfusion to support exercise. In this study, we aim to quantify muscle's vasodilation capability with dynamic BOLD imaging. A deoxyhemoglobin-kinetics model is proposed to analyze dynamic BOLD signals acquired during exercise recovery, deriving a hyperemia index (HI) for a muscle group of interest. We demonstrated the method's validity with calf muscles of healthy subjects who performed plantar flexion for muscle stimulation. In a test with exercise load incrementally increasing from 0 to 16 lbs., gastrocnemius HI showed considerable variance among the 4 subjects, but with a consistent trend, i.e. low at light load (e.g. 0-6 lbs) and linearly increasing at heavy load. The high variability among different subjects was confirmed with the other 10 subjects who exercised with a same moderate load of 8 lbs., with coefficient of variance among subjects' medial gastrocnemius 87.8%, lateral gastrocnemius 111.8% and soleus 132.3%. These findings align with the fact that intensive exercise induces high muscle hyperemia, but a comparison among different subjects is hard to make, presumably due to the subjects' different rate of oxygen utilization. For the same 10 subjects who exercised with load of 8 lbs., we also performed dynamic contrast enhanced (DCE) MRI to measure muscle perfusion (F). With a moderate correlation of 0.654, HI and F displayed three distinctive responses of calf muscles: soleus of all the subjects were in the cluster of low F and low HI, and gastrocnemius of most subjects had high F and either low or high HI. This finding suggests that parameter F encapsulates blood flow through vessels of all sizes, but BOLD-derived HI focuses on capillary flow and therefore is a more specific indicator of muscle vasodilation. In conclusion, the proposed hyperemia index has the potential of quantitatively assessing muscle vasodilation induced with exercise.

Neurological activation during verbal fluency task and resting-state functional connectivity abnormalities in obsessive-compulsive disorder: a functional near-infrared spectroscopy study

Objective

This study aims to investigate the activation of frontotemporal functional brain areas in patients with Obsessive-Compulsive Disorder (OCD) during a Verbal Fluency Task (VFT), and to compare their brain functional connectivity in a resting state with that of healthy controls. The goal is to deepen our understanding of the neuropathological mechanisms underlying OCD.

Methods

32 patients with OCD and 32 controls matched for age, gender, handedness, and years of education participated in this study, they were divided into OCD group and healthy comtrol group. We conducted VFT task tests and 10-minute resting state tests on both groups by using functional Near-Infrared Spectroscopy (fNIRS). The VFT was utilized to assess the activation (beta values) and the integral and centroid values of the frontal and bilateral temporal lobes, including brain areas BA9 and 46 (dorsolateral prefrontal cortex), BA10 (frontal pole), BA45 (inferior frontal gyrus), BA21 (middle temporal gyrus), and BA22 (superior temporal gyrus). We evaluated the functional connectivity levels of these areas during the resting state. Differences in these measures between OCD patients and healthy controls were analyzed using two-sample independent t-tests and non-parametric Mann-Whitney U tests.

Results

During VFT, OCD had smaller integral values(z=5.371, p<0.001; t=4.720, p<0.001), and larger centroid values(t=-2.281, p=0.026; z=-2.182, p=0.029) compared to healthy controls, along with a reduced number of activated channels detected by fNIRS. Additionally, activation values (β) in various functional brain areas, including BA9, BA46, BA10, BA45, BA21, and BA22, were significantly lower in the OCD group (all p< 0.01). In the resting state, notable disparities in functional connectivity were observed between the inferior frontal gyrus (IFG) and dorsolateral prefrontal cortex (DLPFC) in the OCD group, in comparison to the control group. Specifically, there was a significant increase in connectivity between the left IFG and right DLPFC, suggesting the presence of altered connectivity patterns in these areas.

Conclusions

The study highlights significant disparities in neural activation and functional connectivity between OCD patients and healthy controls during VFT. Specifically, reduced activation was noted in the frontal and bilateral temporal lobes of OCD patients, alongside alterations in resting-state functional connectivity between the IFG and DLPFC. These findings contribute to our understanding of the neurobiological underpinnings of OCD and may guide future therapeutic strategies.

Creating anatomically-derived, standardized, customizable, and three-dimensional printable head caps for functional neuroimaging

Significance

Consistent and accurate probe placement is a crucial step towards enhancing the reproducibility of longitudinal and group-based functional neuroimaging studies. While the selection of headgear is central to these efforts, there does not currently exist a standardized design that can accommodate diverse probe configurations and experimental procedures.

Aim

We aim to provide the community with an open-source software pipeline for conveniently creating low-cost, 3-D printable neuroimaging head caps with anatomically significant landmarks integrated into the structure of the cap.

Approach

We utilize our advanced 3-D head mesh generation toolbox and 10-20 head landmark calculations to quickly convert a subject's anatomical scan or an atlas into a 3-D printable head cap model. The 3-D modeling environment of the open-source Blender platform permits advanced mesh processing features to customize the cap. The design process is streamlined into a Blender add-on named "NeuroCaptain".

Results

Using the intuitive user interface, we create various head cap models using brain atlases, and share those with the community. The resulting mesh-based head cap designs are readily 3-D printable using off-the-shelf printers and filaments while accurately preserving the head topology and landmarks.

Conclusions

The methods developed in this work result in a widely accessible tool for community members to design, customize and fabricate caps that incorporate anatomically derived landmarks. This not only permits personalized head cap designs to achieve improved accuracy, but also offers an open platform for the community to propose standardizable head caps to facilitate multi-centered data collection and sharing.

Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders

Introduction

The Next Move in Movement Disorders (NEMO) study is an initiative aimed at advancing our understanding and the classification of hyperkinetic movement disorders, including tremor, myoclonus, dystonia, and myoclonus-dystonia. The study has two main objectives: (a) to develop a computer-aided tool for precise and consistent classification of these movement disorder phenotypes, and (b) to deepen our understanding of brain pathophysiology through advanced neuroimaging techniques. This protocol review details the neuroimaging data acquisition and preprocessing procedures employed by the NEMO team to achieve these goals.

Methods and analysis

To meet the study's objectives, NEMO utilizes multiple imaging techniques, including T1-weighted structural MRI, resting-state fMRI, motor task fMRI, and 18F-FDG PET scans. We will outline our efforts over the past 4 years to enhance the quality of our collected data, and address challenges such as head movements during image acquisition, choosing acquisition parameters and constructing data preprocessing pipelines. This study is the first to employ these neuroimaging modalities in a standardized approach contributing to more uniformity in the analyses of future studies comparing these patient groups. The data collected will contribute to the development of a machine learning-based classification tool and improve our understanding of disorder-specific neurobiological factors.

Ethics and dissemination

Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.

Chemigenetic Far-Red Labels and Ca2+ Indicators Optimized for Photoacoustic Imaging

Photoacoustic imaging is an emerging modality with significant promise for biomedical applications such as neuroimaging, owing to its capability to capture large fields of view deep inside complex scattering tissue. However, widespread adoption of this technique has been hindered by a lack of suitable molecular reporters for this modality. In this work, we introduce chemigenetic labels and calcium sensors specifically tailored for photoacoustic imaging, using a combination of synthetic dyes and HaloTag-based self-labeling proteins. We rationally design and engineer far-red "acoustogenic" dyes, showing high photoacoustic turn-ons upon binding to HaloTag, and develop a suite of tunable calcium indicators based on these scaffolds. These first-generation photoacoustic reporters show excellent performance in tissue-mimicking phantoms, with the best variants outperforming existing sensors in terms of signal intensity, sensitivity, and photostability. We demonstrate the application of these ligands for labeling HaloTag-expressing neurons in mouse brain tissue, producing strong, specifically targeted photoacoustic signal, and provide a first example of in vivo labeling with these chemigenetic photoacoustic probes. Together, this work establishes a new approach for the design of photoacoustic reporters, paving the way toward deep tissue functional imaging.

Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

Mapping eye, arm, and reward information in frontal motor cortices using electrocorticography in non-human primates

Goal-directed reaches give rise to dynamic neural activity across the brain as we move our eyes and arms, and process outcomes. High spatiotemporal resolution mapping of multiple cortical areas will improve our understanding of how these neural computations are spatially and temporally distributed across the brain. In this study, we used micro-electrocorticography (μECoG) recordings in two male monkeys performing visually guided reaches to map information related to eye movements, arm movements, and receiving rewards over a 1.37 cm2 area of frontal motor cortices (primary motor cortex, premotor cortex, frontal eye field, and dorsolateral pre-frontal cortex). Time-frequency and decoding analyses revealed that eye and arm movement information shifts across brain regions during a reach, likely reflecting shifts from planning to execution. We then used phase-based analyses to reveal potential overlaps of eye and arm information. We found that arm movement decoding performance was impacted by task-irrelevant eye movements, consistent with the presence of intermixed eye and arm information across much of motor cortices. Phase-based analyses also identified reward-related activity primarily around the principal sulcus in the pre-frontal cortex as well as near the arcuate sulcus in the premotor cortex. Our results demonstrate μECoG's strengths for functional mapping and provide further detail on the spatial distribution of eye, arm, and reward information processing distributed across frontal cortices during reaching. These insights advance our understanding of the overlapping neural computations underlying coordinated movements and reveal opportunities to leverage these signals to enhance future brain-computer interfaces.

Bridging Neurobiological Insights and Clinical Biomarkers in Postpartum Depression: A Narrative Review

Postpartum depression (PPD) affects 174 million women worldwide and is characterized by profound sadness, anxiety, irritability, and debilitating fatigue, which disrupt maternal caregiving and the mother-infant relationship. Limited pharmacological interventions are currently available. Our understanding of the neurobiological pathophysiology of PPD remains incomplete, potentially hindering the development of novel treatment strategies. Recent hypotheses suggest that PPD is driven by a complex interplay of hormonal changes, neurotransmitter imbalances, inflammation, genetic factors, psychosocial stressors, and hypothalamic-pituitary-adrenal (HPA) axis dysregulation. This narrative review examines recent clinical studies on PPD within the past 15 years, emphasizing advancements in neuroimaging findings and blood biomarker detection. Additionally, we summarize recent laboratory work using animal models to mimic PPD, focusing on hormone withdrawal, HPA axis dysfunction, and perinatal stress theories. We also revisit neurobiological results from several brain regions associated with negative emotions, such as the amygdala, prefrontal cortex, hippocampus, and striatum. These insights aim to improve our understanding of PPD's neurobiological mechanisms, guiding future research for better early detection, prevention, and personalized treatment strategies for women affected by PPD and their families.

Diagnostic value of structural, functional and effective connectivity in bipolar disorder

INTRODUCTION

The aim of this systematic review is to assess the functional magnetic resonance imaging (fMRI) studies of bipolar disorder (BD) patients that characterize differences in terms of structural, functional, and effective connectivity between the patients with BD, patients with other psychiatric disorders and healthy controls as possible biomarkers for diagnosing the disorder using neuroimaging.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), guidelines a systematic search for recent (since 2015) original studies on connectivity in bipolar disorder was conducted in PUBMED and SCOPUS.

RESULTS

A total of 60 studies were included in this systematic review: 20 of the structural connectivity, 33 of the functional connectivity, and only 7 of the studies focused on effective connectivity complied with the inclusion and exclusion criteria.

DISCUSSION

Despite the great heterogeneity in the findings, there are several trends that emerge. In structural connectivity studies, the main abnormalities in bipolar disorder patients were in the frontal gyrus, anterior, as well as posterior cingulate cortex and differences in emotion and reward-related networks. Cerebellum (vermis) to cerebrum functional connectivity was found to be the most common finding in BD. Moreover, prefrontal cortex and amygdala connectivity as part of the rich-club hubs were often reported to be disrupted. The most common findings based on effective connectivity were alterations in salience network, default mode network and executive control network. Although more studies with larger sample sizes are needed to ascertain altered brain connectivity as diagnostic biomarker, there is a perspective that the method could be used as a single marker of diagnosis in the future, and the process of adoption could be accelerated by using approaches such as semiunsupervised machine learning.

Optogenetic Brain-Computer Interfaces

The brain-computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.

The diagnosis of ASD with MRI: a systematic review and meta-analysis

While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the current diagnostic practice involves observation-based criteria. This study is a systematic review and meta-analysis of studies that aim to diagnose ASD using magnetic resonance imaging (MRI). The main objective is to describe the state of the art of diagnosing ASD using MRI in terms of performance metrics and interpretation. Furthermore, subgroups, including different MRI modalities and statistical heterogeneity, are analyzed. Studies that dichotomously diagnose individuals with ASD and healthy controls by analyses progressing from magnetic resonance imaging obtained in a resting state were systematically selected by two independent reviewers. Studies were sought on Web of Science and PubMed, which were last accessed on February 24, 2023. The included studies were assessed on quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate random-effects model was used for syntheses. One hundred and thirty-four studies were included comprising 159 eligible experiments. Despite the overlap in the studied samples, an estimated 4982 unique participants consisting of 2439 individuals with ASD and 2543 healthy controls were included. The pooled summary estimates of diagnostic performance are 76.0% sensitivity (95% CI 74.1-77.8), 75.7% specificity (95% CI 74.0-77.4), and an area under curve of 0.823, but uncertainty in the study assessments limits confidence. The main limitations are heterogeneity and uncertainty about the generalization of diagnostic performance. Therefore, comparisons between subgroups were considered inappropriate. Despite the current limitations, methods progressing from MRI approach the diagnostic performance needed for clinical practice. The state of the art has obstacles but shows potential for future clinical application.

An Integrated Perspective of Effort and Perception of Effort

Effort and the perception of effort (PE) have been extensively studied across disciplines, resulting in multiple definitions. These inconsistencies block scientific progress by impeding effective communication between and within fields. Here, we present an integrated perspective of effort and PE that is applicable to both physical and cognitive activities. We define effort as the energy utilized to perform an action. This definition can be applied to biological entities performing various voluntary or involuntary activities, irrespective of whether the effort contributes to goal achievement. Then, we define PE as the instantaneous experience of utilizing energy to perform an action. This definition builds on that of effort without conflating it with other subjective experiences. We explore the nature of effort and PE as constructs and variables and highlight key considerations in their measurement. Our integrated perspective aims to facilitate a deeper understanding of these constructs, refine research methodologies, and promote interdisciplinary collaborations.

Criticality in Alzheimer's and healthy brains: insights from phase-ordering

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality where complex systems exhibit high correlations. As a system approaches criticality, it develops "domain"-like regions with competing phases and increased spatio-temporal correlations that diverge. The dynamics of these domains depend on the system's proximity to criticality. This study explores the differences in the proximity to criticality of Alzheimer's-afflicted and cognitively normal brains through the use of a spin-lattice model derived from resting-state fMRI data and investigates the type of criticality found in the human brain - whether it is of the Ising class or something more complex. The temporal correlations in both groups display a stretched exponential nature, indicating closer alignment with the criticality of the spin-glass class rather than the Ising class. Longer relaxation times observed in cognitively normal subjects suggest increased proximity to the phase boundary. The weak distinction observed in the spatial characteristics related to proximity to criticality might once more point to a spin-glass scenario, necessitating nuanced order parameters to distinguish between phase-ordering in Alzheimer's and cognitively normal brains.

A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome

Patients with locked-in syndrome (LIS) and complete locked-in syndrome (CLIS) own a fully functional brain restricted within a non-functional body. In order to help LIS patients stay connected with their surroundings, brain-computer interfaces (BCIs) and related technologies have emerged. BCIs translate brain activity into actions that can be performed by external devices enabling LIS patients to communicate, leading to an increase in their quality of life. The past decade has seen the rapid development of BCIs that have the potential to be used for patients with locked-in syndrome, from which a great deal is tested only on healthy subjects and not on actual patients. This study aims to (1) provide the readers with a comprehensive study that contributes to this growing area of research by exploring the performance of BCIs tested specifically on LIS and CLIS patients, (2) give an overview of different modalities and paradigms used in different stages of the locked-in syndrome, and (3) discuss the contributions and limitations of BCIs introduced for the LIS and CLIS patients in the state-of-the-art and lay a groundwork for researchers interested in this field.