Brain Imaging Modalities Research Articles

The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study’s EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.

Posterior reversible encephalopathy syndrome (PRES) on the second postpartum day: learning experience from a case report and literature review

BackgroundPosterior reversible encephalopathy syndrome (PRES) is an uncommon neurological disorder which is characterised by variable symptoms. The transient clinical condition may be underestimated and misdiagnosed as other conditions, especially, among pregnant women with severe preeclampsia, eclampsia, and HELLP (hemolysis, elevated liver enzymes, and low platelets) syndrome in the puerperium. We hereby contribute to the literature this rare complication and hightlight the appropriate management of PRES .Presentation caseA pregnant woman (gravida 3, parity 2) had a normal antenatal course. However, she was diagnosed with severe preeclampsia and HELLP syndrome at 29 weeks and 5 days of gestation. Therefore, she was indicated for a medical termination of pregnancy following a patient’s consent at our tertiary referral hospital. Severely, the patient developed rapidly with altered mental health in early puerperium. In result, PRES was diagnosed based on a brain magnetic resonance imaging (MRI) evidence with typical findings. After a strict multidisciplinary management, the clinical condition improved after 5 days of onset and recovered completely after a 4-month follow-up without any sequelae.ConclusionIn summary, despite its rarity, clinicians ought to be knowledgeable and raise an aware of PRES during pregnancy. Importantly, a brain imaging modalities should be taken into account among pregnant women with neurological symptoms subsequent to severe preeclampsia. In addition to early diagnosis, a timely appropriate treatment with multidisciplinary team is strongly indicated. Further studies with a large case series are required for this uncommon entity.

THE TRANSFORMATIVE FUTURE OF METABOLOMICS FROM DIAGNOSTICS TO THERAPEUTICS

Metabolomics, or the study of metabolites produced during chemical reactions in living organisms, is a fast-growing topic within the "omics" sciences. It has proven valuable in various disciplines, including plant biology and medicine. However, there are still issues to be addressed. Metabolomics has already found biomarkers for multiple disorders, and the emphasis is now turning from diagnosis to treatment, resulting in "therapeutic metabolomics." This innovative approach seeks to comprehend and address the underlying pathophysiology of diseases. The next significant metabolomics hurdle will be identifying safe medicines that selectively target illness causes and demonstrate clinical utility. For example, current prostate cancer research has identified sarcosine pathway components as both disease progression biomarkers and possible therapeutic targets (1). Alongside concurrent brain imaging modalities, new data compilation approaches are needed for the metabolomics-based diagnosis of brain cancers. Furthermore, to expedite the measurement process, hyperpolarization techniques for the C12 isotope of metabolic substrates are required (2). Nowadays, the trend is shifting towards clinical studies. Within the next few years, it will become evident and apparent whether metabolomics can replace other studies that include genomics and proteomics in oncology. Even though metabolomics is increasingly used to determine the etiology of diseases in which metabolic changes play a role, several biomarkers have been identified for several diseases. However, metabolite profiling for many diseasesin adults has still not been focused on and studied (3). In the future, environmental metabolomics will be more concerned with understanding how organisms react to abiotic influences and interact with other organisms. This approach will expand our understanding of ecological interactions, allowing studies to go beyond individual individuals to entire populations. While closely related to ecophysiology and ecology, this research will eventually facilitate genetic adaptation studies, providing insights into how animals evolve and thrive in changing environments (2,3).

Improving source estimation of retinotopic MEG responses by combining data from multiple subjects

Abstract Magnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.

PrecivityAD2™ Blood Test: Analytical Validation of an LC-MS/MS Assay for Quantifying Plasma Phospho-tau217 and Non-Phospho-tau217 Peptide Concentrations That Are Used with Plasma Amyloid-β42/40 in a Multianalyte Assay with Algorithmic Analysis for Detecting Brain Amyloid Pathology.

Alzheimer's disease (AD) is a progressive irreversible neurodegenerative disorder that represents a major global public health concern. Traditionally, AD is diagnosed using cerebrospinal fluid biomarker analysis or brain imaging modalities. Recently, less burdensome, more widely available blood biomarker (BBM) assays for amyloid-beta (Aβ42/40) and phosphorylated-tau concentrations have been found to accurately identify the presence/absence of brain amyloid plaques and tau tangles and have helped to streamline AD diagnosis. However, few BBMs have been rigorously analytically validated. Herein, we report the analytical validation of a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) multiplex method for quantifying plasma phosphorylated-tau217 (p-tau217) and non-phosphorylated-tau217 (np-tau217) peptide concentrations. We combined the p-tau217/np-tau217 concentrations ratio (%p-tau217) and the previously validated LC-MS/MS multiplex assay for plasma Aβ42/40 into a new multianalyte assay with algorithmic analysis (MAAA; PrecivityAD2™ test) that identifies brain amyloid status based on brain amyloid positron emission tomography. We found (a) the %p-tau217 assay is precise, accurate, sensitive, and linear over a wide analytical measurement range, and free from carryover and interference; (b) the pre-analytical specimen collection, processing, storage, and shipping conditions that maintain plasma tau peptide stability; and (c) using the measured analytical imprecision for plasma Aβ42/40 and p-tau217/np-tau217 levels in a worst-case scenario model, the PrecivityAD2 test algorithm for amyloid pathology classification changed for only 3.5% of participants from brain amyloid positive to negative, or from negative to positive. The plasma sample preparation and LC-MS/MS methods underlying the PrecivityAD2 test are suitable for use in the clinical laboratory and valid for the test's intended purpose: to aid in the diagnostic evaluation of individuals aged 55 and older with signs or symptoms of mild cognitive impairment or dementia.

Correlation-Filter-Based Channel and Feature Selection Framework for Hybrid EEG-fNIRS BCI Applications.

The proposed study is based on a feature and channel selection strategy that uses correlation filters for brain-computer interface (BCI) applications using electroencephalography (EEG)-functional near-infrared spectroscopy (fNIRS) brain imaging modalities. The proposed approach fuses the complementary information of the two modalities to train the classifier. The channels most closely correlated with brain activity are extracted using a correlation-based connectivity matrix for fNIRS and EEG separately. Furthermore, the training vector is formed through the identification and fusion of the statistical features of both modalities (i.e., slope, skewness, maximum, skewness, mean, and kurtosis). The constructed fused feature vector is passed through various filters (including ReliefF, minimum redundancy maximum relevance, chi-square test, analysis of variance, and Kruskal-Wallis filters) to remove redundant information before training. Traditional classifiers such as neural networks, support-vector machines, linear discriminant analysis, and ensembles were used for the purpose of training and testing. A publicly available dataset with motor imagery information was used for validation of the proposed approach. Our findings indicate that the proposed correlation-filter-based channel and feature selection framework significantly enhances the classification accuracy of hybrid EEG-fNIRS. The ReliefF-based filter outperformed other filters with the ensemble classifier with a high accuracy of 94.77 ± 4.26%. The statistical analysis also validated the significance (p < 0.01) of the results. A comparison of the proposed framework with the prior findings was also presented. Our results show that the proposed approach can be used in future EEG-fNIRS-based hybrid BCI applications.

Improving photoacoustic imaging through the skull using deep learning: a numerical study

Photoacoustic computed tomography (PACT) has recently emerged as an attractive imaging modality for functional brain imaging due to its rich optical absorption contrast, high spatial and temporal resolutions, and relatively deep penetration. However, a major hurdle in using PACT for the human brain is distortion of the signal due to the skull, which negatively affects the quality of the images. In this project, we aimed to improve transcranial PACT using a U-Net architecture that can minimize distortion from the skull. This numerical study utilized a large collection of blood vessel images obtained from an online database and a computed tomography (CT) scan of an ex vivo human skull. The synthetic photoacoustic radiofrequency data were generated using the open-source wave solver k-Wave. Comparing the images generated by deep learning with the ground truth images, we achieved an average structural similarity index of 0.874 and an average peak signal-to-noise ratio of 17.92 dB.

3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores

Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural network (CNN) models that predicted sub-scores of the 13-item Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS–Cog13) based on MRI and FDG–PET brain-imaging data. Analysis of the trained network showed that each key ADAS–Cog13 sub-score was associated with a specific set of brain features within an imaging modality. Furthermore, different association patterns were observed in MRI and FDG–PET modalities. According to MRI, cognitive sub-scores were typically associated with structural changes of subcortical regions, including amygdala, hippocampus, and putamen. Comparatively, according to FDG–PET, cognitive functions were typically associated with metabolic changes of cortical regions, including the cingulated gyrus, occipital cortex, middle front gyrus, precuneus cortex, and the cerebellum. These findings brought insights into complex AD etiology and emphasized the importance of investigating different brain-imaging modalities.

Relationship between neuroimaging and emotion recognition in mild cognitive impairment patients

ObjectiveDementia is a major public health problem with high needs for early detection, efficient treatment, and prognosis evaluation. Social cognition impairment could be an early dementia indicator and can be assessed with emotion recognition evaluation tests. The purpose of this study is to investigate the link between different brain imaging modalities and cognitive status in Mild Cognitive Impairment (MCI) patients, with the goal of uncovering potential physiopathological mechanisms based on social cognition performance. MethodsThe relationship between the Reading the Mind in the Eyes Test (RMET) and some clinical and biochemical variables ([18 F]FDG PET-CT and anatomical MR parameters, neuropsychological evaluation, and CSF biomarkers) was studied in 166 patients with MCI by using a correlational approach. ResultsThe RMET correlated with neuropsychological variables, as well as with structural and functional brain parameters obtained from the MR and FDG-PET imaging evaluation. However, significant correlations between the RMET and CSF biomarkers were not found. DiscussionDifferent neuroimaging parameters were found to be related to an emotion recognition task in MCI. This analysis identified potential minimally-invasive biomarkers providing some knowledge about the physiopathological mechanisms in MCI.

In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents.

Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution. Here, we investigate in vivo AEBI of different DBS currents to explore the potential of DBS visualization using AEBI. According to the typical DBS stimulus parameters, four types of DBS currents, including time pattern, waveform, frequency, and amplitude are designed to implement AEBI experiments in living rat brains. Based on acoustoelectric (AE) signals, the AEBI images of each type DBS current are explored and the resolution is quantitatively analyzed for performance evaluation. Furtherly, the AE signals are decoded to characterize DBS currents from multiple perspectives, including time-frequency domain, spatial distribution, and amplitude comparation. The results show that in vivo transcranial AEBI can accurately locate the DBS contact position with a millimeter spatial resolution (< 2 mm) and millisecond temporal resolution (< 10 ms). Besides, the decoded AE signal at DBS contact position is capable of describing the corresponding DBS current characteristics and identifying current pattern changes. This study first validates that AEBI can localize in vivo DBS contact and characterize different DBS currents. AEBI is expected to develop into a noninvasive DBS real-time monitoring technology with high spatiotemporal resolution.

Visual Perceptual Processing Abnormalities in Body Dysmorphic Disorder.

Phenomenological observations of individuals with body dysmorphic disorder (BDD), coupled with evidence from neuropsychological, psychophysical, and neuroimaging studies, support a model of aberrant visual perception characterized by deficient global/holistic, enhanced detail/local processing, and selective visual-attentional biases. These features may contribute to the core symptomatology of distorted perception of their appearance, in addition to misinterpretation of others' facial expressions and poor insight regarding their misperceived appearance defects. Insights from visual processing studies can contribute to the development of novel interventions, such as perceptual retraining and non-invasive neuromodulation. However, much remains to be understood about visual perception in BDD. Future research should leverage brain imaging modalities with high temporal resolutions and employ study designs that induce conflicts in multisensory integration, thereby advancing our mechanistic understanding of distorted visual perception observed in BDD.

Baseline and Longitudinal Changes in Lateral Ventricle Volume Predict Cognitive Decline

AbstractBackgroundThe use of biomarkers for early detection of Alzheimer disease (AD) is crucial for developing potential treatments. Previous research has linked structural alterations in specific brain regions to cognitive decline. Lateral ventricle volume can be estimated reliably from various modalities of brain imaging, but this has not been well studied as an AD biomarker. In this study, we tested the hypothesis that baseline and longitudinal changes in lateral ventricle volume as measured by MRI could predict changes in global cognitive composite scores.Method363 Participants enrolled in longitudinal studies at the Knight ADRC at Washington University in St. Louis were included. Magnetic resonance imaging (MRI) data was obtained through OASIS (www.oasis‐brains.org). Participants were required to have at least two cognitive assessments and MRI scans. Lateral ventricular volumes were extracted and corrected for head size using the FreeSurfer intracranial volume. A global cognitive composite score was calculated from the average z‐scores of 10 neuropsychological tests. A random coefficient model was used to calculate longitudinal change in lateral ventricular volumes, and to assess whether these measures predicted cognitive decline when adjusted for age, sex, APOE Ɛ4, and years of education. Spearman correlation was also used to evaluate the relationship between cognitive decline and baseline or longitudinal changes in lateral ventricle volumes.ResultWe found that baseline and longitudinal changes in lateral ventricle volume predicted the rate of change in global cognitive composite (Table 1 and 2, t = ‐4.82, p&lt;0.0001 and t = ‐3.83, p = 0.0001). Additionally, there were significant correlations between baseline and longitudinal changes in lateral ventricle volume and the rate of change in global cognitive composite (Figure 1, R2 = 0.0657, p&lt;0.0001; and R2 = 0.0412, p&lt;0.0001).ConclusionBaseline and longitudinal changes in lateral ventricle volume predicted decline on a global cognitive composite. MRI scanners are widely available world‐wide. Baseline and longitudinal lateral ventricle volumes could potentially be used to stratify risk for cognitive decline for clinical trials.

Baseline and Longitudinal Changes in Lateral Ventricle Volume Predict Cognitive Decline

AbstractBackgroundThe use of biomarkers for early detection of Alzheimer disease (AD) is crucial for developing potential treatments. Previous research has linked structural alterations in specific brain regions to cognitive decline. Lateral ventricle volume can be estimated reliably from various modalities of brain imaging, but this has not been well studied as an AD biomarker. In this study, we tested the hypothesis that baseline and longitudinal changes in lateral ventricle volume as measured by MRI could predict changes in global cognitive composite scores.Method363 Participants enrolled in longitudinal studies at the Knight ADRC at Washington University in St. Louis were included. Magnetic resonance imaging (MRI) data was obtained through OASIS (www.oasis‐brains.org). Participants were required to have at least two cognitive assessments and MRI scans. Lateral ventricular volumes were extracted and corrected for head size using the FreeSurfer intracranial volume. A global cognitive composite score was calculated from the average z‐scores of 10 neuropsychological tests. A random coefficient model was used to calculate longitudinal change in lateral ventricular volumes, and to assess whether these measures predicted cognitive decline when adjusted for age, sex, APOE Ɛ4, and years of education. Spearman correlation was also used to evaluate the relationship between cognitive decline and baseline or longitudinal changes in lateral ventricle volumes.ResultWe found that baseline and longitudinal changes in lateral ventricle volume predicted the rate of change in global cognitive composite (Table 1 and 2, t = ‐4.82, p&lt;0.0001 and t = ‐3.83, p = 0.0001). Additionally, there were significant correlations between baseline and longitudinal changes in lateral ventricle volume and the rate of change in global cognitive composite (Figure 1, R2 = 0.0657, p&lt;0.0001; and R2 = 0.0412, p&lt;0.0001).ConclusionBaseline and longitudinal changes in lateral ventricle volume predicted decline on a global cognitive composite. MRI scanners are widely available world‐wide. Baseline and longitudinal lateral ventricle volumes could potentially be used to stratify risk for cognitive decline for clinical trials.

Both clinically informed and brain region agnostic approaches identify neuroimaging derived phenotypes associated with genetically regulated gene expression of late‐onset Alzheimer’s disease genes

AbstractBackgroundGenetically regulated gene expression (GReX) data leverages expression quantitative trait loci to investigate the genetic mechanism of Alzheimer’s disease (AD). This study utilized the GReX‐mediated neuro‐imaging derived phenotypes (NIDPs), summary features derived from brain imaging modalities, as an endophenotype to identify brain regions associated with late‐onset AD (LOAD) via gene expression.MethodThis study contained brain region‐agnostic and region‐informed approaches. Both approaches utilized a set of 25 genes causally associated with LOAD in our previous cross‐tissue transcriptome‐wide association studies (TWAS) (Chen et al., 2021) to identify LOAD‐associated GReX‐mediated NIDPs. We leveraged the UK Biobank (UKBB) neuroimaging TWAS resource to provide GReX‐mediated NIDP information. Region‐agnostic: We assessed associations between GReX of LOAD genes and T1‐derived cortical and subcortical grey matter NIDPs from the UKBB neuroimaging TWAS. Region‐informed: We identified a set of AD‐associated cortical grey matter NIDPs from diagnosed AD patients (Westman et al., 2013). Next, we subset the UKBB neuroimaging TWAS data to those describing associations between GReX of LOAD genes and the selected AD NIDPs.A Benjamini‐Hochberg FDR p‐value threshold of 0.05 was used to establish statistical significance, and causal inference testing on associations between GReX of LOAD genes and NIDPs was conducted by implementing MR‐JTI in both approaches.Result Region‐agnostic: This approach found associations between GReX of 8 LOAD‐associated genes and 14 NIDPs (Figure 1). The most significant association is the GreX of ZNF296 and the right pars opercularis of the inferior frontal cortex, which replicated across 16 different tissues. Region‐informed: A single highly significant causal association between the GReX of PSMC3 and the ventral diencephalon was identified and replicated in gene expression models of 3 different bansal ganglia‐derived tissue models.ConclusionOur study shows the highly significant causal association between the GReX of ZNF296 and the right pars opercularis on LOAD through the region‐agnostic approach and the strong causal association between the GReX of PSMC3 and the ventral diencephalon on LOAD in AD‐associated region‐informed analyses. Our methodologies provide opportunities to explore brain regions associated with LOAD that are mediated by GReX of AD‐associated genes illuminating the impact of LOAD‐associated genes on gene expression and brain physiology.

Non‐Invasive Temporal Interference Hippocampal Stimulation in Early Alzheimer’s Disease

AbstractBackgroundLarge‐scale efforts to reverse or stop the progression of Alzheimer’s disease (AD) and associated cognitive decline have failed to produce viable treatment options, highlighting the need for new interventional strategies. A significant feature of AD is aberrant changes in brain network activity and dynamics, closely coupled with functional impairment and pathophysiology in deep brain structures, such as the hippocampus. In this study, we are investigating the effects of multi‐session, non‐invasive temporal interference (TI) electrical stimulation of the hippocampus in early‐stage AD patients. This follows our recent work where we demonstrated the efficacy of TI electrical stimulation to non‐invasively target and modulate activity in the hippocampus of healthy young adults and improve episodic memory performance.MethodThe study consists of transcranial TI stimulation of the hippocampus of 24 early‐stage AD patients daily, in 10 consecutive sessions. We assess the safety and tolerability of the stimulation via questionnaires, interviews and brain scans. Additionally, we explore effects on the pathophysiology and resting state brain dynamics, using a battery of brain imaging modalities: positron emission tomography (PET) of mitochondrial complex‐1, blood oxygen level‐dependent functional magnetic resonance imaging (BOLD fMRI), diffusion tensor imaging (DTI) and arterial spin labelling (ASL). Finally, we explore the effects on cognitive performance using a battery of neurocognitive assessments.ResultHerein, we will report the outcome of the study’s interim analysis pertaining to safety, neuroimaging and behaviour modulation outcome measures.ConclusionHerein, we will report the conclusion of the study’s interim analysis pertaining to safety, neuroimaging and behaviour modulation outcome measures.

Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm

Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.

Invited Symposium 1: Traumatic Brain Injury: Highlighting the Contributions of Dr. Harvey S. Levin Ph.D., ABPP-CN, FACSM 1946 - 2022

Invited Symposium 1: Traumatic Brain Injury: Highlighting the Contributions of Dr. Harvey S. Levin Ph.D., ABPP-CN, FACSM 1946 - 2022

Roadmap for C9ORF72 in Frontotemporal Dementia and Amyotrophic Lateral Sclerosis: Report on the C9ORF72 FTD/ALS Summit.

A summit held March 2023 in Scottsdale, Arizona (USA) focused on the intronic hexanucleotide expansion in the C9ORF72 gene and its relevance in frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS; C9ORF72-FTD/ALS). The goal of this summit was to connect basic scientists, clinical researchers, drug developers, and individuals affected by C9ORF72-FTD/ALS to evaluate how collaborative efforts across the FTD-ALS disease spectrum might break down existing disease silos. Presentations and discussions covered recent discoveries in C9ORF72-FTD/ALS disease mechanisms, availability of disease biomarkers and recent advances in therapeutic development, and clinical trial design for prevention and treatment for individuals affected by C9ORF72-FTD/ALS and asymptomatic pathological expansion carriers. The C9ORF72-associated hexanucleotide repeat expansion is an important locus for both ALS and FTD. C9ORF72-FTD/ALS may be characterized by loss of function of the C9ORF72 protein and toxic gain of functions caused by both dipeptide repeat (DPR) proteins and hexanucleotide repeat RNA. C9ORF72-FTD/ALS therapeutic strategies discussed at the summit included the use of antisense oligonucleotides, adeno-associated virus (AAV)-mediated gene silencing and gene delivery, and engineered small molecules targeting RNA structures associated with the C9ORF72 expansion. Neurofilament light chain, DPR proteins, and transactive response (TAR) DNA-binding protein 43 (TDP-43)-associated molecular changes were presented as biomarker candidates. Similarly, brain imaging modalities (i.e., magnetic resonance imaging [MRI] and positron emission tomography [PET]) measuring structural, functional, and metabolic changes were discussed as important tools to monitor individuals affected with C9ORF72-FTD/ALS, at both pre-symptomatic and symptomatic disease stages. Finally, summit attendees evaluated current clinical trial designs available for FTD or ALS patients and concluded that therapeutics relevant to FTD/ALS patients, such as those specifically targeting C9ORF72, may need to be tested with composite endpoints covering clinical symptoms of both FTD and ALS. The latter will require novel clinical trial designs to be inclusive of all patient subgroups spanning the FTD/ALS spectrum.

Functional magnetic particle imaging (fMPI) of cerebrovascular changes in the rat brain during hypercapnia

Objective. Non-invasive functional brain imaging modalities are limited in number, each with its own complex trade-offs between sensitivity, spatial and temporal resolution, and the directness with which the measured signals reflect neuronal activation. Magnetic particle imaging (MPI) directly maps the cerebral blood volume (CBV), and its high sensitivity derives from the nonlinear magnetization of the superparamagnetic iron oxide nanoparticle (SPION) tracer confined to the blood pool. Our work evaluates functional MPI (fMPI) as a new hemodynamic functional imaging modality by mapping the CBV response in a rodent model where CBV is modulated by hypercapnic breathing manipulation. Approach. The rodent fMPI time-series data were acquired with a mechanically rotating field-free line MPI scanner capable of 5 s temporal resolution and 3 mm spatial resolution. The rat’s CBV was modulated for 30 min with alternating 5 min hyper-/hypocapnic states, and processed using conventional fMRI tools. We compare our results to fMRI responses undergoing similar hypercapnia protocols found in the literature, and reinforce this comparison in a study of one rat with 9.4T BOLD fMRI using the identical protocol. Main results. The initial image in the time-series showed mean resting brain voxel SNR values, averaged across rats, of 99.9 following the first 10 mg kg−1 SPION injection and 134 following the second. The time-series fit a conventional General Linear Model with a 15%–40% CBV change and a peak pixel CNR between 12 and 29, 2–6× higher than found in fMRI. Significance. This work introduces a functional modality with high sensitivity, although currently limited spatial and temporal resolution. With future clinical-scale development, a large increase in sensitivity could supplement other modalities and help transition functional brain imaging from a neuroscience tool focusing on population averages to a clinically relevant modality capable of detecting differences in individual patients.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.