Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions.

Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.

Artificial Intelligence Applications in Pediatric Brain Tumor Imaging: A Systematic Review.

OBJECTIVE: Artificial intelligence (AI) has facilitated the analysis of medical imaging given increased computational capacity and medical data availability in recent years. Although many applications for AI in the imaging of brain tumors have been proposed, their potential clinical impact remains to be explored. A systematic review was performed to examine the role of AI in the analysis of pediatric brain tumor imaging. METHODS: PubMed, Embase, and Scopus were searched for relevant articles up to January 27, 2021. RESULTS: Literature search identified 298 records, of which 22 studies were included. The most commonly studied tumors were posterior fossa tumors including brainstem glioma, ependymoma, medulloblastoma, and pilocytic astrocytoma (15, 68%). Tumor diagnosis was the most frequently performed task (14, 64%), followed by tumor segmentation (3, 14%) and tumor detection (3, 14%). Of the 6 studies comparing AI to clinical experts, 5 demonstrated superiority of AI for tumor diagnosis. Other tasks including tumor segmentation, attenuation correction of positron emission tomography scans, image registration for patient positioning, and dose calculation for radiotherapy were performed with high accuracy comparable with clinical experts. No studies described use of the AI tool in routine clinical practice. CONCLUSIONS: AI methods for analysis of pediatric brain tumor imaging have increased exponentially in recent years. However, adoption of these methods in clinical practice requires further characterization of validity and utility. Implementation of these methods may streamline clinical workflows by improving diagnostic accuracy and automating basic imaging analysis tasks.

Neuroimaging in Frontotemporal Dementia: Heterogeneity and Relationships with Underlying Neuropathology.

Frontotemporal dementia encompasses a group of clinical syndromes defined pathologically by degeneration of the frontal and temporal lobes. Historically, these syndromes have been challenging to diagnose, with an average of about three years between the time of symptom onset and the initial evaluation and diagnosis. Research in the field of neuroimaging has revealed numerous biomarkers of the various frontotemporal dementia syndromes, which has provided clinicians with a method of narrowing the differential diagnosis and improving diagnostic accuracy. As such, neuroimaging is considered a core investigative tool in the evaluation of neurodegenerative disorders. Furthermore, patterns of neurodegeneration correlate with the underlying neuropathological substrates of the frontotemporal dementia syndromes, which can aid clinicians in determining the underlying etiology and improve prognostication. This review explores the advancements in neuroimaging and discusses the phenotypic and pathologic features of behavioral variant frontotemporal dementia, semantic variant primary progressive aphasia, and nonfluent variant primary progressive aphasia, as seen on structural magnetic resonance imaging and positron emission tomography.

Structural Neuroimaging Findings Related to Adult Non-CNS Cancer and Treatment: Review, Integration, and Implications for Treatment of Cognitive Dysfunction.

Cancer- and treatment-related cognitive dysfunction (CRCD) is a common challenge faced by patients diagnosed with non-central nervous system (CNS) cancer. It has become increasingly recognized that multiple factors likely play a role in these symptoms, including the cancer disease process, systemic treatments (e.g., chemotherapy and endocrine therapies), and risk factors that may predispose an individual to both cancer and cognitive dysfunction. As the field has evolved, advanced neuroimaging techniques have been applied to better understand the neural correlates of CRCD. This review focuses on structural neuroimaging findings related to CRCD in adult non-CNS cancer populations, including examination of gray matter volume/density and white matter integrity differences between cancer patients and comparison groups, as well as emerging findings regarding structural network abnormalities. Overall, this literature has demonstrated consistent findings of reduced gray matter volume/density and white matter integrity in cancer patients relative to comparison groups. These are most prominent in individuals treated with chemotherapy, though alterations have also been noted in those treated with anti-estrogen and androgen-deprivation therapies. Alterations in gray and white matter structural network connectivity have also been identified. These structural abnormalities have been observed most prominently in frontal and temporal brain regions, and have been shown to correlate with subjective and objective cognitive function, as well as with physiological and clinical variables, helping to inform understanding of CRCD mechanisms. To date, however, structural neuroimaging techniques have not been utilized in systematic studies of potential CRCD treatments, suggesting a potentially fruitful avenue for future research.

Emerging Neuroimaging Biomarkers Across Disease Stage in Parkinson Disease: A Review.

Importance: Imaging biomarkers in Parkinson disease (PD) are increasingly important for monitoring progression in clinical trials and also have the potential to improve clinical care and management. This Review addresses a critical need to make clear the temporal relevance for diagnostic and progression imaging biomarkers to be used by clinicians and researchers over the clinical course of PD. Magnetic resonance imaging (diffusion imaging, neuromelanin-sensitive imaging, iron-sensitive imaging, T1-weighted imaging), positron emission tomography/single-photon emission computed tomography dopaminergic, serotonergic, and cholinergic imaging as well as metabolic and cerebral blood flow network neuroimaging biomarkers in the preclinical, prodromal, early, and moderate to late stages are characterized. Observations: If a clinical trial is being carried out in the preclinical and prodromal stages, potentially useful disease-state biomarkers include dopaminergic imaging of the striatum; metabolic imaging; free-water, neuromelanin-sensitive, and iron-sensitive imaging in the substantia nigra; and T1-weighted structural magnetic resonance imaging. Disease-state biomarkers that can distinguish atypical parkinsonisms are metabolic imaging, free-water imaging, and T1-weighted imaging; dopaminergic imaging and other molecular imaging track progression in prodromal patients, whereas other established progression biomarkers need to be evaluated in prodromal cohorts. Progression in early-stage PD can be monitored using dopaminergic imaging in the striatum, metabolic imaging, and free-water and neuromelanin-sensitive imaging in the posterior substantia nigra. Progression in patients with moderate to late-stage PD can be monitored using free-water imaging in the anterior substantia nigra, R2* of substantia nigra, and metabolic imaging. Cortical thickness and gyrification might also be useful markers or predictors of progression. Dopaminergic imaging and free-water imaging detect progression over 1 year, whereas other modalities detect progression over 18 months or longer. The reliability of progression biomarkers varies with disease stage, whereas disease-state biomarkers are relatively consistent in individuals with preclinical, prodromal, early, and moderate to late-stage PD. Conclusions and Relevance: Imaging biomarkers for various stages of PD are readily available to be used as outcome measures in clinical trials and are potentially useful in multimodal combination with routine clinical assessment. This Review provides a critically important template for considering disease stage when implementing diagnostic and progression biomarkers in both clinical trials and clinical care settings.

Reducing SAR in 7T brain fMRI by circumventing fat suppression while removing the lipid signal through a parallel acquisition approach.

Ultra-high-field functional magnetic resonance imaging (fMRI) offers a way to new insights while increasing the spatial and temporal resolution. However, a crucial concern in 7T human MRI is the increase in power deposition, supervised through the specific absorption rate (SAR). The SAR limitation can restrict the brain coverage or the minimal repetition time of fMRI experiments. In the majority of today's studies fMRI relies on the well-known gradient-echo echo-planar imaging (GRE-EPI) sequence, which offers ultrafast acquisition. Commonly, the GRE-EPI sequence comprises two pulses: fat suppression and excitation. This work provides the means for a significant reduction in the SAR by circumventing the fat-suppression pulse. Without this fat-suppression, however, lipid signal can result in artifacts due to the chemical shift between the lipid and water signals. Our approach exploits a reconstruction similar to the simultaneous-multi-slice method to separate the lipid and water images, thus avoiding undesired lipid artifacts in brain images. The lipid-water separation is based on the known spatial shift of the lipid signal, which can be detected by the multi-channel coils sensitivity profiles. Our study shows robust human imaging, offering greater flexibility to reduce the SAR, shorten the repetition time or increase the volume coverage with substantial benefit for brain functional studies.

Developing the ATX(N) classification for use across the Alzheimer disease continuum.

Breakthroughs in the development of highly accurate fluid and neuroimaging biomarkers have catalysed the conceptual transformation of Alzheimer disease (AD) from the traditional clinical symptom-based definition to a clinical-biological construct along a temporal continuum. The AT(N) system is a symptom-agnostic classification scheme that categorizes individuals using biomarkers that chart core AD pathophysiological features, namely the amyloid-ß (Aß) pathway (A), tau-mediated pathophysiology (T) and neurodegeneration (N). This biomarker matrix is now expanding towards an ATX(N) system, where X represents novel candidate biomarkers for additional pathophysiological mechanisms such as neuroimmune dysregulation, synaptic dysfunction and blood-brain barrier alterations. In this Perspective, we describe the conceptual framework and clinical importance of the existing AT(N) system and the evolving ATX(N) system. We provide a state-of-the-art summary of the potential contexts of use of these systems in AD clinical trials and future clinical practice. We also discuss current challenges related to the validation, standardization and qualification process and provide an outlook on the real-world application of the AT(N) system.

The human connectome in Alzheimer disease - relationship to biomarkers and genetics.

The pathology of Alzheimer disease (AD) damages structural and functional brain networks, resulting in cognitive impairment. The results of recent connectomics studies have now linked changes in structural and functional network organization in AD to the patterns of amyloid-ß and tau accumulation and spread, providing insights into the neurobiological mechanisms of the disease. In addition, the detection of gene-related connectome changes might aid in the early diagnosis of AD and facilitate the development of personalized therapeutic strategies that are effective at earlier stages of the disease spectrum. In this article, we review studies of the associations between connectome changes and amyloid-ß and tau pathologies as well as molecular genetics in different subtypes and stages of AD. We also highlight the utility of connectome-derived computational models for replicating empirical findings and for tracking and predicting the progression of biomarker-indicated AD pathophysiology.

The multiple sclerosis prodrome.

A prodrome is an early set of signs, symptoms or other findings that occur before the onset of typical symptoms of a disease. Prodromal phases are well recognized in several neurological and inflammatory diseases, but the possibility of a prodrome in multiple sclerosis (MS) has received relatively little attention until the past few years. In this Perspective, we summarize what is currently known about the MS prodrome, including its possible duration, clinical features and potential biomarkers. We also consider what insights and lessons can be learned from knowledge of and research into the prodromal phases of other diseases. A better understanding of the MS prodrome could have profound clinical implications as it could enable earlier recognition of MS and earlier initiation of treatments that reduce relapse rates and long-term disability. Knowledge of the MS prodrome could also affect research into the causes of MS, and putative risk factors must be re-evaluated in light of the MS prodrome. We conclude by outlining the major knowledge gaps and propose future initiatives.

Perinatal stroke: mapping and modulating developmental plasticity.

Most cases of hemiparetic cerebral palsy are caused by perinatal stroke, resulting in lifelong disability for millions of people. However, our understanding of how the motor system develops following such early unilateral brain injury is increasing. Tools such as neuroimaging and brain stimulation are generating informed maps of the unique motor networks that emerge following perinatal stroke. As a focal injury of defined timing in an otherwise healthy brain, perinatal stroke represents an ideal human model of developmental plasticity. Here, we provide an introduction to perinatal stroke epidemiology and outcomes, before reviewing models of developmental plasticity after perinatal stroke. We then examine existing therapeutic approaches, including constraint, bimanual and other occupational therapies, and their potential synergy with non-invasive neurostimulation. We end by discussing the promise of exciting new therapies, including novel neurostimulation, brain-computer interfaces and robotics, all focused on improving outcomes after perinatal stroke.

The emergent relationship between temporoparietal junction and anosognosia in Alzheimer's disease.

Anosognosia and impairment of insight are characteristic features of Alzheimer's disease (AD), which can lead to delays in appropriate medical care and significant family discord. The default mode network (DMN), a distributed but highly connected network of brain regions more active during rest than during task, is integrally involved in awareness. DMN dysfunction is common in AD, and disrupted communication between memory-related and self-related DMN networks is associated with anosognosia in AD patients. In addition, the temporoparietal junction (TPJ) is a key region of the "social brain" and also contributes to representations of the self. The exact classification of the TPJ within the DMN is unclear, though connections between the TPJ and DMN have been highlighted in multiple avenues of research. Here we discuss the relationship between the TPJ, DMN, and AD, as well as the potential involvement of the TPJ in anosognosia in AD. We review past and present findings to raise attention to the TPJ, with a specific emphasis on neuroimaging technologies which suggest a pivotal role of the TPJ within large-scale brain networks linked to anosognosia in AD.

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.

Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. This systematic review synthesizes and analyzes the current state of machine learning applications to glioma MRI data and explores the use of machine learning for systematic review automation. Various datapoints were extracted from the 153 studies that met inclusion criteria and analyzed. Natural language processing (NLP) analysis involved keyword extraction, topic modeling and document classification. Machine learning has been applied to tumor grading and diagnosis, tumor segmentation, non-invasive genomic biomarker identification, detection of progression and patient survival prediction. Model performance was generally strong (AUC = 0.87 ± 0.09; sensitivity = 0.87 ± 0.10; specificity = 0.0.86 ± 0.10; precision = 0.88 ± 0.11). Convolutional neural network, support vector machine and random forest algorithms were top performers. Deep learning document classifiers yielded acceptable performance (mean 5-fold cross-validation AUC = 0.71). Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.

Spinocerebellar ataxia clinical trials: opportunities and challenges.

The spinocerebellar ataxias (SCAs) are a group of dominantly inherited diseases that share the defining feature of progressive cerebellar ataxia. The disease process, however, is not confined to the cerebellum; other areas of the brain, in particular, the brainstem, are also affected, resulting in a high burden of morbidity and mortality. Currently, there are no disease-modifying treatments for the SCAs, but preclinical research has led to the development of therapeutic agents ripe for testing in patients. Unfortunately, due to the rarity of these diseases and their slow and variable progression, there are substantial hurdles to overcome in conducting clinical trials. While the epidemiological features of the SCAs are immutable, the feasibility of conducting clinical trials is being addressed through a combination of strategies. These include improvements in clinical outcome measures, the identification of imaging and fluid biomarkers, and innovations in clinical trial design. In this review, we highlight current challenges in initiating clinical trials for the SCAs and also discuss pathways for researchers and clinicians to mitigate these challenges and harness opportunities for clinical trial development.

Recent Advances in Neuroimaging of Epilepsy.

Human neuroimaging has had a major impact on the biological understanding of epilepsy and the relationship between pathophysiology, seizure management, and outcomes. This review highlights notable recent advancements in hardware, sequences, methods, analyses, and applications of human neuroimaging techniques utilized to assess epilepsy. These structural, functional, and metabolic assessments include magnetic resonance imaging (MRI), positron emission tomography (PET), and magnetoencephalography (MEG). Advancements that highlight non-invasive neuroimaging techniques used to study the whole brain are emphasized due to the advantages these provide in clinical and research applications. Thus, topics range across presurgical evaluations, understanding of epilepsy as a network disorder, and the interactions between epilepsy and comorbidities. New techniques and approaches are discussed which are expected to emerge into the mainstream within the next decade and impact our understanding of epilepsies. Further, an increasing breadth of investigations includes the interplay between epilepsy, mental health comorbidities, and aberrant brain networks. In the final section of this review, we focus on neuroimaging studies that assess bidirectional relationships between mental health comorbidities and epilepsy as a model for better understanding of the commonalities between both conditions.

Prodromal Parkinson disease subtypes - key to understanding heterogeneity.

In Parkinson disease (PD), pathological processes and neurodegeneration begin long before the cardinal motor symptoms develop and enable clinical diagnosis. In this prodromal phase, risk and prodromal markers can be used to identify individuals who are likely to develop PD, as in the recently updated International Parkinson and Movement Disorders Society research criteria for prodromal PD. However, increasing evidence suggests that clinical and prodromal PD are heterogeneous, and can be classified into subtypes with different clinical manifestations, pathomechanisms and patterns of spatial and temporal progression in the CNS and PNS. Genetic, pathological and imaging markers, as well as motor and non-motor symptoms, might define prodromal subtypes of PD. Moreover, concomitant pathology or other factors, including amyloid-ß and tau pathology, age and environmental factors, can cause variability in prodromal PD. Patients with REM sleep behaviour disorder (RBD) exhibit distinct patterns of α-synuclein pathology propagation and might indicate a body-first subtype rather than a brain-first subtype. Identification of prodromal PD subtypes and a full understanding of variability at this stage of the disease is crucial for early and accurate diagnosis and for targeting of neuroprotective interventions to ensure efficacy.

Neuroimaging Biomarkers of Chronic Traumatic Encephalopathy: Targets for the Academic Memory Disorders Clinic.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts, such as those from contact sports. The pathognomonic lesion for CTE is the perivascular accumulation of hyper-phosphorylated tau in neurons and other cell process at the depths of sulci. CTE cannot be diagnosed during life at this time, limiting research on risk factors, mechanisms, epidemiology, and treatment. There is an urgent need for in vivo biomarkers that can accurately detect CTE and differentiate it from other neurological disorders. Neuroimaging is an integral component of the clinical evaluation of neurodegenerative diseases and will likely aid in diagnosing CTE during life. In this qualitative review, we present the current evidence on neuroimaging biomarkers for CTE with a focus on molecular, structural, and functional modalities routinely used as part of a dementia evaluation. Supporting imaging-pathological correlation studies are also presented. We targeted neuroimaging studies of living participants at high risk for CTE (e.g., aging former elite American football players, fighters). We conclude that an optimal tau PET radiotracer with high affinity for the 3R/4R neurofibrillary tangles in CTE has not yet been identified. Amyloid PET scans have tended to be negative. Converging structural and functional imaging evidence together with neuropathological evidence show frontotemporal and medial temporal lobe neurodegeneration, and increased likelihood for a cavum septum pellucidum. The literature offers promising neuroimaging biomarker targets of CTE, but it is limited by cross-sectional studies of small samples where the presence of underlying CTE is unknown. Imaging-pathological correlation studies will be important for the development and validation of neuroimaging biomarkers of CTE.

Repetitive Transcranial Magnetic Stimulation as a Therapeutic and Probe in Schizophrenia: Examining the Role of Neuroimaging and Future Directions.

Schizophrenia is a complex condition associated with perceptual disturbances, decreased motivation and affect, and disrupted cognition. Individuals living with schizophrenia may experience myriad poor outcomes, including impairment in independent living and function as well as decreased life expectancy. Though existing treatments may offer benefit, many individuals still experience treatment resistant and disabling symptoms. In light of the negative outcomes associated with schizophrenia and the limitations in currently available treatments, there is a significant need for novel therapeutic interventions. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique that can modulate the activity of discrete cortical regions, allowing direct manipulation of local brain activation and indirect manipulation of the target's associated neural networks. rTMS has been studied in schizophrenia for the treatment of auditory hallucinations, negative symptoms, and cognitive deficits, with mixed results. The field's inability to arrive at a consensus on the use rTMS in schizophrenia has stemmed from a variety of issues, perhaps most notably the significant heterogeneity amongst existing trials. In addition, it is likely that factors specific to schizophrenia, rather than the rTMS itself, have presented barriers to the interpretation of existing results. However, advances in approaches to rTMS as a biologic probe and therapeutic, many of which include the integration of neuroimaging with rTMS, offer hope that this technology may still play a role in improving the understanding and treatment of schizophrenia.

Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease.

Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.