Brain white matter shape changes in amyotrophic lateral sclerosis (ALS): a fractal dimension study

Emerging perspectives of synaptic biomarkers in ALS and FTD

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenerative diseases with shared pathological features like transactive response DNA-binding protein of 43 kDa (TDP-43) inclusions and genetic mutations. Both diseases involve synaptic dysfunction, contributing to their clinical features. Synaptic biomarkers, representing proteins associated with synaptic function or structure, offer insights into disease mechanisms, progression, and treatment responses. These biomarkers can detect disease early, track its progression, and evaluate therapeutic efficacy. ALS is characterized by elevated neurofilament light chain (NfL) levels in cerebrospinal fluid (CSF) and blood, correlating with disease progression. TDP-43 is another key ALS biomarker, its mislocalization linked to synaptic dysfunction. In FTD, TDP-43 and tau proteins are studied as biomarkers. Synaptic biomarkers like neuronal pentraxins (NPs), including neuronal pentraxin 2 (NPTX2), and neuronal pentraxin receptor (NPTXR), offer insights into FTD pathology and cognitive decline. Advanced technologies, like machine learning (ML) and artificial intelligence (AI), aid biomarker discovery and drug development. Challenges in this research include technological limitations in detection, variability across patients, and translating findings from animal models. ML/AI can accelerate discovery by analyzing complex data and predicting disease outcomes. Synaptic biomarkers offer early disease detection, personalized treatment strategies, and insights into disease mechanisms. While challenges persist, technological advancements and interdisciplinary efforts promise to revolutionize the understanding and management of ALS and FTD. This review will explore the present comprehension of synaptic biomarkers in ALS and FTD and discuss their significance and emphasize the prospects and obstacles.

Fractal Dimension Studies of the Brain Shape in Aging and Neurodegenerative Diseases

The fractal dimension is a morphometric measure that has been used to investigate the changes of brain shape complexity in aging and neurodegenerative diseases. This chapter reviews fractal dimension studies in aging and neurodegenerative disorders in the literature. Research has shown that the fractal dimension of the left cerebral hemisphere increases until adolescence and then decreases with aging, while the fractal dimension of the right hemisphere continues to increase until adulthood. Studies in neurodegenerative diseases demonstrated a decline in the fractal dimension of the gray matter and white matter in Alzheimer's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia. In multiple sclerosis, the white matter fractal dimension decreases, but conversely, the fractal dimension of the gray matter increases at specific stages of disease. There is also a decline in the gray matter fractal dimension in frontotemporal dementia and multiple system atrophy of the cerebellar type and in the white matter fractal dimension in epilepsy and stroke. Region-specific changes in fractal dimension have also been found in Huntington's disease and Parkinson's disease. Associations were found between the fractal dimension and clinical scores, showing the potential of the fractal dimension as a marker to monitor brain shape changes in normal or pathological processes and predict cognitive or motor function.

Fractals in Neuroimaging

Several natural phenomena can be described by studying their statistical scaling patterns, hence leading to simple geometrical interpretation. In this regard, fractal geometry is a powerful tool to describe the irregular or fragmented shape of natural features, using spatial or time-domain statistical scaling laws (power-law behavior) to characterize real-world physical systems. This chapter presents some works on the usefulness of fractal features, mainly the fractal dimension and the related Hurst exponent, in the characterization and identification of pathologies and radiological features in neuroimaging, mainly, magnetic resonance imaging.

Fractal dimension: analyzing its potential as a neuroimaging biomarker for brain tumor diagnosis using machine learning

Purpose: The main purpose of this study was to comprehensively investigate the potential of fractal dimension (FD) measures in discriminating brain gliomas into low-grade glioma (LGG) and high-grade glioma (HGG) by examining tumor constituents and non-tumorous gray matter (GM) and white matter (WM) regions. Methods: Retrospective magnetic resonance imaging (MRI) data of 42 glioma patients (LGG, n = 27 and HGG, n = 15) were used in this study. Using MRI, we calculated different FD measures based on the general structure, boundary, and skeleton aspects of the tumorous and non-tumorous brain GM and WM regions. Texture features, namely, angular second moment, contrast, inverse difference moment, correlation, and entropy, were also measured in the tumorous and non-tumorous regions. The efficacy of FD features was assessed by comparing them with texture features. Statistical inference and machine learning approaches were used on the aforementioned measures to distinguish LGG and HGG patients. Results: FD measures from tumorous and non-tumorous regions were able to distinguish LGG and HGG patients. Among the 15 different FD measures, the general structure FD values of enhanced tumor regions yielded high accuracy (93%), sensitivity (97%), specificity (98%), and area under the receiver operating characteristic curve (AUC) score (98%). Non-tumorous GM skeleton FD values also yielded good accuracy (83.3%), sensitivity (100%), specificity (60%), and AUC score (80%) in classifying the tumor grades. These measures were also found to be significantly (p < 0.05) different between LGG and HGG patients. On the other hand, among the 25 texture features, enhanced tumor region features, namely, contrast, correlation, and entropy, revealed significant differences between LGG and HGG. In machine learning, the enhanced tumor region texture features yielded high accuracy, sensitivity, specificity, and AUC score. Conclusion: A comparison between texture and FD features revealed that FD analysis on different aspects of the tumorous and non-tumorous components not only distinguished LGG and HGG patients with high statistical significance and classification accuracy but also provided better insights into glioma grade classification. Therefore, FD features can serve as potential neuroimaging biomarkers for glioma.

Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.

Longitudinal Effects of Combination Antiretroviral Therapy on Cognition and Neuroimaging Biomarkers in Treatment-Naïve People with HIV

BACKGROUND AND OBJECTIVES

While combination antiretroviral therapy (cART) has dramatically increased the life expectancy of people with HIV (PWH), nearly 50% develop HIV-associated neurocognitive disorders (HAND)¹. This may be due to previously uncontrolled HIV viral replication, immune activation maintained by residual viral replication² or activation from other sources^{3, 4}, or cART-associated neurotoxicity⁵. The aim of this study was to determine the effect of cART on cognition and neuroimaging biomarkers markers in people with HIV (PWH) before and after initiation of cART compared to HIV negative controls (HC) and HIV elite controllers (EC) who remain untreated.

METHODS

We recruited three groups of participants from the University of Rochester, McGovern Medical School and SUNY Upstate Medical University: 1) ART-treatment-naïve PWH; 2) age-matched HC; and 3) EC. Participants underwent brain MRI and clinical and neuropsychological assessments at baseline, one year, and two years. PWH were also assessed 12 weeks after initiating cART. Volumetric analysis and fractal dimensionality (FD) were calculated for cortical and subcortical regions. Mixed effect regressions examined the effect of group and imaging variables on cognition.

RESULTS

We enrolled 47 PWH, 58 HC, and 10 EC. At baseline, PWH had worse cognition and lower cortical volumes than HC. Cognition improved following initiation of cART and remained stable over time. Greater cortical thickness was associated with better cognition at baseline; greater FD of parietal, temporal and occipital lobes was associated with better cognition at baseline and longitudinally. At baseline, EC had worse cognition, lower cortical thickness and lower FD in all four lobes and caudate than PWH and HC. Greater cortical thickness, hippocampal volumes and FD of frontal, temporal and occipital lobes were associated with better cognition longitudinally.

CONCLUSIONS

Initiation of cART in PWH is associated with improvement in brain structure and cognition. However, significant differences persist over time compared to HC. Similar trends in EC suggest that results are due to HIV infection rather than treatment. Stronger associations between cognition and FD suggest this imaging metric may be a more sensitive marker of neuronal injury than cortical thickness and volumetric measures.

Fractal dimension of the brain in neurodegenerative disease and dementia: A systematic review

Sensitive and specific antemortem biomarkers of neurodegenerative disease and dementia are crucial to the pursuit of effective treatments, required both to reliably identify disease and to track its progression. Atrophy is the structural magnetic resonance imaging (MRI) hallmark of neurodegeneration. However in most cases it likely indicates a relatively advanced stage of disease less susceptible to treatment as some disease processes begin decades prior to clinical onset. Among emerging metrics that characterise brain shape rather than volume, fractal dimension (FD) quantifies shape complexity. FD has been applied in diverse fields of science to measure subtle changes in elaborate structures. We review its application thus far to structural MRI of the brain in neurodegenerative disease and dementia. We identified studies involving subjects who met criteria for mild cognitive impairment, Alzheimer's Disease, Vascular Dementia, Lewy Body Dementia, Frontotemporal Dementia, Amyotrophic Lateral Sclerosis, Parkinson's Disease, Huntington's Disease, Multiple Systems Atrophy, Spinocerebellar Ataxia and Multiple Sclerosis. The early literature suggests that neurodegenerative disease processes are usually associated with a decline in FD of the brain. The literature includes examples of disease-related change in FD occurring independently of atrophy, which if substantiated would represent a valuable advantage over other structural imaging metrics. However, it is likely to be non-specific and to exhibit complex spatial and temporal patterns. A more harmonious methodological approach across a larger number of studies as well as careful attention to technical factors associated with image processing and FD measurement will help to better elucidate the metric's utility.

Graph theory network analysis provides brain MRI evidence of a partial continuum of neurodegeneration in patients with UMN-predominant ALS and ALS-FTD

OBJECTIVE

Our routine clinical neuroimaging showed hyperintense signal along the corticospinal tract only in some but not all patients with upper motor neuron (UMN)-predominant ALS. ALS patients with CST hyperintensity (ALS-CST+) and those without CST hyperintensity (ALS-CST-) present with nearly identical clinical UMN-predominant symptoms. Some previous studies have suggested that ALS patients with frontotemporal dementia (FTD) are on a continuum with ALS patients without FTD, while others have not. We aimed to determine whether: (a) ALS-CST+, ALS-CST-, and ALS-FTD patients show differential sites of predominant neurodegeneration occurring primarily cortically in the perikaryon or subcortically in the white matter (WM), or (b) UMN-predominant ALS is on a continuum with ALS-FTD.

METHODS

Exploratory whole brain grey matter (GM) voxel-based morphometry and WM network analysis using graph theory approach were performed. In this exploratory study, MRI data from 58 ALS patients (ALS-FTD, n = 15; ALS-CST+, n = 19; ALS-CST-, n = 24) and 14 neurological controls were obtained.

RESULTS

Significant differences in degree measures (evaluating WM networks) were observed between ALS patients and controls in frontal, motor, extra-motor, subcortical, and cerebellar regions. GM atrophy was observed only in the ALS-FTD subgroup and not in the other ALS subgroups.

CONCLUSION

Although WM network disruption by the ALS disease process showed different patterns between ALS-CST+, ALS-CST-, and ALS-FTD subgroups, there were some overlaps, particularly in prefrontal regions and between ALS-CST+ and ALS-FTD patients. Our preliminary findings suggest a partial continuum of, at least, WM degeneration between these subgroups with predominance of cortical pathology ("neuronopathy") in ALS-FTD patients and subcortical WM pathology ("axonopathy") in ALS-CST+ and ALS-CST- patients.

Effect of alpha-mangostin in the prevention of behavioural and neurochemical defects in methylmercury-induced neurotoxicity in experimental rats

Methylmercury (MeHg+) is a known neurotoxin that causes progressive motor neuron degeneration in the central nervous system. Axonal degeneration, oligodendrocyte degeneration, and myelin basic protein (MBP) deficits are among the neuropathological abnormalities caused by MeHg+ in amyotrophic lateral sclerosis (ALS). This results in demyelination and motor neuron death in both humans and animals. Previous experimental studies have confirmed that overexpression of the extracellular signalling regulated kinase (ERK1/2) signalling contributes to glutamate excitotoxicity, inflammatory response of microglial cells, and oligodendrocyte (OL) dysfunction that promotes myelin loss. Alpha-mangostin (AMG), an active ingredient obtained from the tree "Garcinia mangostana Linn," has been used in experimental animals to treat a variety of brain disorders, including Parkinson's and Huntington's disease memory impairment, Alzheimer's disease, and schizophrenia, including Parkinson's disease and Huntington's disease memory impairment, Alzheimer's disease, and schizophrenia. AMG has traditionally been used as an antioxidant, anti-inflammatory, and neuroprotective agent.Accordingly, we investigated the therapeutic potential of AMG (100 and 200 mg/kg) in experimental rats with methylmercury (MeHg+)-induced neurotoxicity. The neuroprotective effect of AMG on behavioural, cellular, molecular, and other gross pathological changes, such as histopathological alterations in MeHg+ -treated rat brains, is presented. The neurological behaviour of experimental rats was evaluated using a Morris water maze (MWM), open field test (OFT), grip strength test (GST), and force swim test (FST). In addition, we investigate AMG's neuroprotective effect by restoring MBP levels in cerebral spinal fluid and whole rat brain homogenate. The apoptotic, pro-inflammatory, and oxidative stress markers were measured in rat blood plasma samples and brain homogenate. According to the findings of this study, AMG decreases ERK-1/2 levels and modulates neurochemical alterations in rat brains, minimising MeHg+ -induced neurotoxicity.

Protective effects of apigenin on methylmercury-induced behavioral/neurochemical abnormalities and neurotoxicity in rats

Methylmercury (MeHg) is a neurotoxin that induces neurotoxicity and cell death in neurons. MeHg increases oligodendrocyte death, glial cell activation, and motor neuron demyelination in the motor cortex and spinal cord. As a result, MeHg plays an important role in developing neurocomplications similar to amyotrophic lateral sclerosis (ALS). Recent research has implicated c-JNK and p38MAPK overactivation in the pathogenesis of ALS. Apigenin (APG) is a flavonoid having anti-inflammatory, antioxidant, and c-JNK/p38MAPK inhibitory activities. The purpose of this study is to determine whether APG possesses neuroprotective effects in MeHg-induced neurotoxicity in adult rats associated with ALS-like neuropathological alterations. In the current study, the neurotoxin MeHg causes an ALS-like phenotype in Wistar rats after 21 days of oral administration at a dose of 5 mg/kg. Prolonged administration of APG (40 and 80 mg/kg) improved neurobehavioral parameters such as learning memory, cognition, motor coordination, and grip strength. This is mainly associated with the downregulation of c-JNK and p38MAPK signaling as well as the restoration of myelin basic protein within the brain. Furthermore, APG inhibited neuronal apoptotic markers (Bax, Bcl-2, and caspase-3), restored neurotransmitter imbalance, decreased inflammatory markers (TNF- and IL-1), and alleviated oxidative damage. As a result, the current study shows that APG has neuroprotective potential as a c-JNK and p38MAPK signaling inhibitor against MeHg-induced neurotoxicity in adult rats. Based on these promising findings, we suggested that APG could be a potential new therapeutic approach over other conventional therapeutic approaches for MeHg-induced neurotoxicity in neurobehavioral, molecular, and neurochemical abnormalities.

BrainFD: Measuring the Intracranial Brain Volume With Fractal Dimension

A few methods and tools are available for the quantitative measurement of the brain volume targeting mainly brain volume loss. However, several factors, such as the clinical conditions, the time of the day, the type of MRI machine, the brain volume artifacts, the pseudoatrophy, and the variations among the protocols, produce extreme variations leading to misdiagnosis of brain atrophy. While brain white matter loss is a characteristic lesion during neurodegeneration, the main objective of this study was to create a computational tool for high precision measuring structural brain changes using the fractal dimension (FD) definition. The validation of the BrainFD software is based on T1-weighted MRI images from the Open Access Series of Imaging Studies (OASIS)-3 brain database, where each participant has multiple MRI scan sessions. The software is based on the Python and JAVA programming languages with the main functionality of the FD calculation using the box-counting algorithm, for different subjects on the same brain regions, with high accuracy and resolution, offering the ability to compare brain data regions from different subjects and on multiple sessions, creating different imaging profiles based on the Clinical Dementia Rating (CDR) scores of the participants. Two experiments were executed. The first was a cross-sectional study where the data were separated into two CDR classes. In the second experiment, a model on multiple heterogeneous data was trained, and the FD calculation for each participant of the OASIS-3 database through multiple sessions was evaluated. The results suggest that the FD variation efficiently describes the structural complexity of the brain and the related cognitive decline. Additionally, the FD efficiently discriminates the two classes achieving 100% accuracy. It is shown that this classification outperforms the currently existing methods in terms of accuracy and the size of the dataset. Therefore, the FD calculation for identifying intracranial brain volume loss could be applied as a potential low-cost personalized imaging biomarker. Furthermore, the possibilities measuring different brain areas and subregions could give robust evidence of the slightest variations to imaging data obtained from repetitive measurements to Physicians and Radiologists.

Guggulsterone ameliorates ethidium bromide-induced experimental model of multiple sclerosis via restoration of behavioral, molecular, neurochemical and morphological alterations in rat brain

Multiple Sclerosis (MS) is a progressive neurodegenerative disease with clinical signs of neuroinflammation and the central nervous system's demyelination. Numerous studies have identified the role of the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) overexpression and the low level of peroxisome proliferator-activated receptor-gamma (PPAR-γ) in MS pathogenesis. Guggulsterone (GST), an active component derived from 'Commiphora Mukul,' has been used to treat various diseases. Traditional uses indicate that GST is a suitable agent for anti-inflammatory action. Therefore, we assessed the therapeutic potential of GST (30 and 60 mg/kg) in ethidium bromide (EB) induced demyelination in experimental rats and investigated the molecular mechanism by modulating the JAK/STAT and PPAR-γ receptor signaling. Wistar rats were randomly divided into six groups (n = 6). EB (0.1%/10 μl) was injected selectively in the intracerebropeduncle (ICP) region for seven days to cause MS-like manifestations. The present study reveals that long-term administration of GST for 28 days has a neuroprotective effect by improving behavioral deficits (spatial cognition memory, grip, and motor coordination) associated with lower STAT-3 levels. While elevating PPAR-γ and myelin basic protein levels in rat brains are consistent with the functioning of both signaling pathways. Also, GST modulates the neurotransmitter level by increasing Ach, dopamine, serotonin and by reducing glutamate. Moreover, GST ameliorates inflammatory cytokines (TNF, IL-1β), and oxidative stress markers (AchE, SOD, catalase, MDA, GSH, nitrite). In addition, GST prevented apoptosis, as demonstrated by the reduction of caspase-3 and Bax. Simultaneously, Bcl-2 elevation and the restoration of gross morphology alterations are also recovered by long-term GST treatment. Therefore, it can be concluded that GST may be a potential alternative drug candidate for MS-related motor neuron dysfunctions.

Nrf2/HO-1 Signaling Activator Acetyl-11-keto-beta Boswellic Acid (AKBA)-Mediated Neuroprotection in Methyl Mercury-Induced Experimental Model of ALS

Methylmercury (MeHg) is a potent neurotoxin that causes neurotoxicity and neuronal cell death. MeHg exposure also leads to oligodendrocyte destruction, glial cell overactivation, and demyelination of motor neurons in the motor cortex and spinal cord. As a result, MeHg plays an important role in the progression of amyotrophic lateral sclerosis (ALS)-like neurocomplications. ALS is a fatal neurodegenerative disorder in which neuroinflammation is the leading cause of further CNS demyelination. Nuclear factor erythroid-2-related factor-2 (Nrf2)/Heme oxygenase-1 (HO-1) signaling pathway was thought to be a potential target for neuroprotection in ALS. Acetyl-11-keto-beta-boswellic acid (AKBA) is a multi-component pentacyclic triterpenoid mixture derived from Boswellia serrata with anti-inflammatory and antioxidant properties. The research aimed to investigate whether AKBA, as a Nrf2 / HO-1 activator, can provide protection against ALS. Thus, we explored the role of AKBA on the Nrf2/HO-1 signaling pathway in a MeHg-induced experimental ALS model. In this study, ALS was induced in Wistar rats by oral gavage of MeHg 5 mg/kg for 21 days. An open field test, force swim test, and grip strength were performed to observe experimental rats' motor coordination behaviors. In contrast, a morris water maze was performed for learning and memory. Administration of AKBA 50 mg/kg and AKBA 100 mg/kg continued from day 22 to 42. Neurochemical parameters were evaluated in the rat's brain homogenate. In the meantime, post-treatment with AKBA significantly improved behavioral, neurochemical, and gross pathological characteristics in the brain of rats by increasing the amount of Nrf2/HO-1 in brain tissue. Collectively, our findings indicated that AKBA could potentially avoid demyelination and encourage remyelination.

Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients

BACKGROUND

Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain's fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS).

METHODS

We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis.

RESULTS

There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035-0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043-4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317-0.955; Harrell C 0.59; Wald p 0.0029].

CONCLUSIONS

Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.

Corticospinal Tract and Related Grey Matter Morphometric Shape Analysis in ALS Phenotypes: A Fractal Dimension Study

A pathological hallmark of amyotrophic lateral sclerosis (ALS) is corticospinal tract (CST) degeneration resulting in upper motor neuron (UMN) dysfunction. No quantitative test is available to easily assess UMN pathways. Brain neuroimaging in ALS promises to potentially change this through identifying biomarkers of UMN dysfunction that may accelerate diagnosis and track disease progression. Fractal dimension (FD) has successfully been used to quantify brain grey matter (GM) and white matter (WM) shape complexity in various neurological disorders. Therefore, we investigated CST and whole brain GM and WM morphometric changes using FD analyses in ALS patients with different phenotypes. We hypothesized that FD would detect differences between ALS patients and neurologic controls and even between the ALS subgroups. Neuroimaging was performed in neurologic controls (n = 14), and ALS patients (n = 75). ALS patients were assigned into four groups based on their clinical or radiographic phenotypes. FD values were estimated for brain WM and GM structures. Patients with ALS and frontotemporal dementia (ALS-FTD) showed significantly higher CST FD values and lower primary motor and sensory cortex GM FD values compared to other ALS groups. No other group of ALS patients revealed significant FD value changes when compared to neurologic controls or with other ALS patient groups. These findings support a more severe disease process in ALS-FTD patients compared to other ALS patient groups. FD value measures may be a sensitive index to evaluate GM and WM (including CST) degeneration in ALS patients.

Neuroprotective Effect of α-Mangostin in the Ameliorating Propionic Acid-Induced Experimental Model of Autism in Wistar Rats

Several studies have documented the role of hyper-activation of extracellular signal-regulated kinases (ERK) in Autism pathogenesis. Alpha-mangostin (AMG) is a phytoconstituents with anti-oxidants, anti-inflammatory, and ERK inhibition properties in many diseases. Our research aims to investigate the neuroprotective effect of AMG in the rat model of intracerebroventricular-propionic acid (ICV-PPA) induced autism with a confirmation of its effect on the ERK signaling. Autism was induced in Wistar rats (total 36 rats; 18 male/18 female) by multiple doses of PPA through ICV injection for 11 days. Actophotometer and beam walking tasks were used to evaluate animals’ motor abilities, and the Morris water maze task was utilized to confirm the cognition and memory in animals. Long term administration of AMG100 mg/kg and AMG200 mg/kg continued from day 12 to day 44 of the experiment. Before that, animals were sacrificed, brains isolated, morphological, gross pathological studies were performed, and neurochemical analysis was performed in the brain homogenates. Cellular and molecular markers, including ERK, myelin basic protein, apoptotic markers including caspase-3, Bax, Bcl-2, neuroinflammatory markers, neurotransmitters, and oxidative stress markers, have been tested throughout the brain. Thus, AMG reduces the overactivation of the ERK signaling and also restored autism-like behavioral and neurochemical alterations.

Brain Cortical Complexity Alteration in Amyotrophic Lateral Sclerosis: A Preliminary Fractal Dimensionality Study

Objective

Fractal dimensionality (FD) analysis provides a quantitative description of brain structural complexity. The application of FD analysis has provided evidence of amyotrophic lateral sclerosis- (ALS-) related white matter degeneration. This study is aimed at evaluating, for the first time, FD alterations in a gray matter in ALS and determining its association with clinical parameters. Materials and Methods. This study included 22 patients diagnosed with ALS and 20 healthy subjects who underwent high-resolution T1-weighted imaging scanning. Disease severity was assessed using the revised ALS Functional Rating Scale (ALSFRS-R). The duration of symptoms and rate of disease progression were also assessed. The regional FD value was calculated by a computational anatomy toolbox and compared among groups. The relationship between cortical FD values and clinical parameters was evaluated by Spearman correlation analysis.

Results

ALS patients showed decreased FD values in the left precentral gyrus and central sulcus, left circular sulcus of insula (superior segment), left cingulate gyrus and sulcus (middle-posterior part), right precentral gyrus, and right postcentral gyrus. The FD values in the right precentral gyrus were positively correlated to ALSFRS-R scores (r = 0.44 and P = 0.023), whereas negatively correlated to the rate of disease progression (r = 0.44 and P = 0.023), whereas negatively correlated to the rate of disease progression (r = 0.44 and P = 0.023), whereas negatively correlated to the rate of disease progression (

Conclusions

Our results suggest an ALS-related reduction in structural complexity involving the gray matter. FD analysis may shed more light on the pathophysiology of ALS.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

2-Deoxy-2-[18 F]fluoro-d-glucose positron emission tomography, cortical thickness and white matter graph network abnormalities in brains of patients with amyotrophic lateral sclerosis and frontotemporal dementia suggest early neuronopathy rather than axonopathy

BACKGROUND AND PURPOSE

Amyotrophic lateral sclerosis (ALS) is a motor neuron disorder, although extra-motor degeneration is well recognized, especially in frontotemporal regions manifested as ALS with frontotemporal dementia (ALS-FTD). Previous neuroimaging studies of the brains of ALS-FTD patients have measured abnormalities of either grey matter (GM) or white matter (WM) structures but not of both together. Therefore, the aim was to evaluate both GM and WM in the same ALS-FTD patient using functional and structural neuroimaging. By doing so, insights could be gained into whether neurodegeneration in ALS-FTD is primarily a neuronopathy or axonopathy.

METHODS

After high-resolution brain 2-deoxy-2-[¹⁸ F]fluoro-D-glucose (¹⁸ F-FDG) positron emission tomography (PET) and magnetic resonance imaging (MRI) scans were obtained in ALS-FTD patients and in age- and sex-matched neurological controls, changes in metabolic rate, cortical thickness (CT) and WM network analysis using graph theory were analyzed.

RESULTS

Significant reductions in ¹⁸ F-FDG PET metabolism, CT and WM connections were observed in motor and extra-motor brain regions of ALS-FTD patients compared to controls. Both CT and underlying WM networks were abnormal in frontal, temporal, parietal and occipital lobes of ALS-FTD patients with 86 of 90 brain regions showing reductions of CT.

CONCLUSION

Abnormalities in significantly fewer WM networks underlying the affected cortical regions suggest that neurodegeneration in brains of ALS-FTD patients is primarily a 'neuronopathy' rather than an 'axonopathy.'

Fractal dimension brain morphometry: a novel approach to quantify white matter in traumatic brain injury

Traumatic brain injury (TBI) is the main cause of disability in people younger than 35 in the United States. The mechanisms of TBI are complex resulting in both focal and diffuse brain damage. Fractal dimension (FD) is a measure that can characterize morphometric complexity and variability of brain structure especially white matter (WM) structure and may provide novel insights into the injuries evident following TBI. FD-based brain morphometry may provide information on WM structural changes after TBI that is more sensitive to subtle structural changes post injury compared to conventional MRI measurements. Anatomical and diffusion tensor imaging (DTI) data were obtained using a 3 T MRI scanner in subjects with moderate to severe TBI and in healthy controls (HC). Whole brain WM volume, grey matter volume, cortical thickness, cortical area, FD and DTI metrics were evaluated globally and for the left and right hemispheres separately. A neuropsychological test battery sensitive to cognitive impairment associated with traumatic brain injury was performed. TBI group showed lower structural complexity (FD) bilaterally (p < 0.05). No significant difference in either grey matter volume, cortical thickness or cortical area was observed in any of the brain regions between TBI and healthy controls. No significant differences in whole brain WM volume or DTI metrics between TBI and HC groups were observed. Behavioral data analysis revealed that WM FD accounted for a significant amount of variance in executive functioning and processing speed beyond demographic and DTI variables. FD therefore, may serve as a sensitive marker of injury and may play a role in outcome prediction in TBI.

Applying the D50 disease progression model to gray and white matter pathology in amyotrophic lateral sclerosis

•

The D50 disease progression model well characterized a cross-sectional ALS cohort.

•

VBM reveled ALS-related widespread gray and white matter density decreases.

•

A spread of structural alterations occurs along with D50 model derived disease phases.

•

White-matter alterations were associated with higher disease aggressiveness.

Therapeutic management and research in Amyotrophic Laterals Sclerosis (ALS) have been limited by the substantial heterogeneity in progression and anatomical spread that are endemic of the disease. Neuroimaging biomarkers represent powerful additions to the current monitoring repertoire but have yielded inconsistent associations with clinical scores like the ALS functional rating scale. The D50 disease progression model was developed to address limitations with clinical indices and the difficulty obtaining longitudinal data in ALS. It yields overall disease aggressiveness as time taken to reach halved functionality (D50); individual disease covered in distinct phases; and calculated functional state and calculated functional loss as acute descriptors of local disease activity. It greatly reduces the noise of the ALS functional rating scale and allows the comparison of highly heterogeneous disease and progression subtypes. In this study, we performed Voxel-Based Morphometry for 85 patients with ALS (60.1 ± 11.5 years, 36 female) and 62 healthy controls. Group-wise comparisons were performed separately for gray matter and white matter using ANCOVA testing with threshold-free cluster enhancement. ALS-related widespread gray and white matter density decreases were observed in the bilateral frontal and temporal lobes (p < 0.001, family-wise error corrected). We observed a progressive spread of structural alterations along the D50-derived phases, that were primarily located in frontal, temporal and occipital gray matter areas, as well as in supratentorial neuronal projections (p < 0.001 family-wise error corrected). ALS patients with higher overall disease aggressiveness (D50 < 30 months) showed a distinct pattern of supratentorial white matter density decreases relative to patients with lower aggressiveness; no significant differences were observed for gray matter density (p < 0.001 family-wise error corrected).

The application of the D50 disease progression model separates measures of disease aggressiveness from disease accumulation. It revealed a strong correlation between disease phases and in-vivo measures of cerebral structural integrity. This study underscores the proposed corticofugal spread of cerebral pathology in ALS. We recommend application of the D50 model in studies linking clinical data with neuroimaging correlates.

Unbiased MRI Analyses Identify Micropathologic Differences Between Upper Motor Neuron-Predominant ALS Phenotypes

Amyotrophic lateral sclerosis (ALS) is an incurable and progressively fatal neurodegenerative disease that manifests with distinct clinical phenotypes, which are seen in neuroimaging, and clinical studies. T2- and proton density (PD)-weighted magnetic resonance imaging (MRI) displays hyperintense signal along the corticospinal tract (CST) in some ALS patients with upper motor neuron (UMN)-predominant signs. These patients tend to be younger and have significantly faster disease progression. We hypothesize that such ALS patients with CST hyperintensity (ALS-CST+) comprise a clinical subtype distinct from other ALS subtypes, namely patients with UMN-predominant ALS without CST hyperintensity, classic ALS, and ALS with frontotemporal dementia (FTD). Novel approaches such as fractal dimension analysis on conventional MRI (cMRI) and advanced MR techniques such as diffusion tensor imaging (DTI) reveal significant differences between ALS-CST+ and the aforementioned ALS subtypes. Our unbiased neuroimaging studies demonstrate that the ALS-CST+ group, which can be initially identified by T2-, PD-, and FLAIR-weighted cMRI, is distinctive and distinguishable from other ALS subtypes with possible differences in disease pathogenesis.

Quantitative analysis of grey matter degeneration in FTD patients using fractal dimension analysis

Fractal dimension (FD) is a quantitative parameter that can characterizes the complexity of human brain tissue. Extensive grey matter (GM) pathology has been previously identified in Frontotemporal dementia (FTD) and its variants. The aim of the present study was to investigate the GM morphometric abnormalities in the behavioral variant FTD (bvFTD) and primary progressive aphasia (PPA) using FD analysis. Twenty-seven bvFTD, 12 PPA and 20 controls were studied. SPM8 was used to segment the brain into GM tissue. Then the FD values were estimated for the GM skeleton, surface and general structure in patients and controls using our previously published algorithm. We found that patients with bvFTD had significant reduction in FD values of skeleton and general structure when compared to controls. In PPA, more significant decrease in FD was noted in the whole brain and left hemisphere skeleton along with left hemisphere general structure. Only the right hemisphere skeleton had a significant correlation with total score of Frontal Systems Behavior Scale (FrSBe). The results showed that the variants of FTD are associated with disease specific morphometric complexity patterns. These results indicate that FD can be used as a biomarker for the structural changes associated with neurodegenerative diseases.

White matter volume loss in amyotrophic lateral sclerosis: A meta-analysis of voxel-based morphometry studies

Structural neuroimaging studies of white matter (WM) volume in amyotrophic lateral sclerosis (ALS) using voxel-based morphometry (VBM) have yielded inconsistent findings. This study aimed to perform a quantitative voxel-based meta-analysis using effect-size signed differential mapping (ES-SDM) to establish a statistical consensus between published studies for WM volume alterations in ALS. The pooled meta-analysis revealed significant WM volume losses in the bilateral supplementary motor areas (SMAs), bilateral precentral gyri (PGs), left middle cerebellar peduncle and right cerebellum in patients with ALS, involving the corticospinal tract (CST), interhemispheric fibers, subcortical arcuate fibers, projection fibers to the striatum and cortico-ponto-cerebellar tract. The meta-regression showed that the ALS functional rating scale-revised (ALSFRS-R) was positively correlated with decreased WM volume in the bilateral SMAs, whereas illness duration was negatively correlated with WM volume reduction in the right SMA. This study provides a thorough profile of WM volume loss in ALS and robust evidence that ALS is a multisystem neurodegenerative disease that involves a variety of subcortical WM tracts extending beyond motor cortex involvement.

Age is reflected in the Fractal Dimensionality of MRI Diffusion Based Tractography

Fractal analysis is a widely used tool to analyze the geometrical complexity of biological structures. The geometry of natural objects such as plants, clouds, cellular structures, blood vessel, and many others cannot be described sufficiently with Euclidian geometric properties, but can be represented by a parameter called the fractal dimension. Here we show that a specific estimate of fractal dimension, the correlation dimension, is able to describe changes in the structural complexity of the human brain, based on data from magnetic resonance diffusion imaging. White matter nerve fiber bundles, represented by tractograms, were analyzed with regards to geometrical complexity, using fractal geometry. The well-known age-related change of white matter tissue was used to verify changes by means of fractal dimension. Structural changes in the brain were successfully be observed and quantified by fractal dimension and compared with changes in fractional anisotropy.

Complexity analysis of cortical surface detects changes in future Alzheimer's disease converters

Alzheimer's disease (AD) is a neurological disorder that creates neurodegenerative changes at several structural and functional levels in human brain tissue. The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of the human brain. In this study, we investigate spherical harmonic-based FD (SHFD), thickness, and local gyrification index (LGI) to assess whether they identify cortical surface abnormalities toward the conversion to AD. We study 33 AD patients, 122 mild cognitive impairment (MCI) patients (50 MCI converters and 29 MCI nonconverters), and 32 healthy controls (HC). SHFD, thickness, and LGI methodology allowed us to perform not only global level but also local level assessments in each cortical surface vertex. First, we found that global SHFD decreased in AD and future MCI converters compared to HC, and in MCI converters compared to MCI nonconverters. Second, we found that local white matter SHFD was reduced in AD compared to HC and MCI mainly in medial temporal lobe. Third, local white-matter SHFD was significantly reduced in MCI converters compared to MCI nonconverters in distributed areas, including the medial frontal lobe. Thickness and LGI metrics presented a reduction in AD compared to HC. Thickness was significantly reduced in MCI converters compared to healthy controls in entorhinal cortex and lateral temporal. In summary, SHFD was the only surface measure showing differences between MCI individuals that will convert or remain stable in the next 4 years. We suggest that SHFD may be an optimal complement to thickness loss analysis in monitoring longitudinal changes in preclinical and clinical stages of AD. Hum Brain Mapp 38:5905-5918, 2017. © 2017 Wiley Periodicals, Inc.

Morphology of the pancreas in type 2 diabetes: effect of weight loss with or without normalisation of insulin secretory capacity

AIMS/HYPOTHESIS

This study was designed to establish whether the low volume and irregular border of the pancreas in type 2 diabetes would be normalised after reversal of diabetes.

METHODS

A total of 29 individuals with type 2 diabetes undertook a very low energy (very low calorie) diet for 8 weeks followed by weight maintenance for 6 months. Methods were established to quantify the pancreas volume and degree of irregularity of the pancreas border. Three-dimensional volume-rendering and fractal dimension (FD) analysis of the MRI-acquired images were employed, as was three-point Dixon imaging to quantify the fat content.

RESULTS

There was no change in pancreas volume 6 months after reversal of diabetes compared with baseline (52.0 ± 4.9 cm(3) and 51.4 ± 4.5 cm(3), respectively; p = 0.69), nor was any volumetric change observed in the non-responders. There was an inverse relationship between the volume and fat content of the pancreas in the total study population (r =-0.50, p = 0.006). Reversal of diabetes was associated with an increase in irregularity of the pancreas borders between baseline and 8 weeks (FD 1.143 ± 0.013 and 1.169 ± 0.006, respectively; p = 0.05), followed by a decrease at 6 months (1.130 ± 0.012, p = 0.006). On the other hand, no changes in FD were seen in the non-reversed group.

CONCLUSIONS/INTERPRETATION

Restoration of normal insulin secretion did not increase the subnormal pancreas volume over 6 months in the study population. A significant change in irregularity of the pancreas borders occurred after acute weight loss only after reversal of diabetes. Pancreas morphology in type 2 diabetes may be prognostically important, and its relationship to change in beta cell function requires further study.

Quantification of structural changes in acute inflammation by fractal dimension, angular second moment and correlation

The aim of the study was to examine alteration and possible application of fractal dimension, angular second moment, and correlation for quantification of structural changes in acutely inflamed tissue. Acute inflammation was induced by injection of turpentine oil into the right and left hind limb muscles of mice, whereas control animals received intramuscular saline injection. After 12 h, animals were anesthetised and treated muscles collected. The tissue was stained by hematoxylin and eosin, digital micrographs produced, enabling determination of fractal dimension of the cells, angular second moment and correlation of studied tissue. Histopathological analysis showed presence of inflammatory infiltrate and tissue damage in inflammatory group, whereas tissue structure in control group was preserved, devoid of inflammatory infiltrate. Fractal dimension of the cells, angular second moment and correlation of treated tissue in inflammatory group decreased in comparison to the control group. In this study, we were first to observe and report that fractal dimension of the cells, angular second moment, and correlation were reduced in acutely inflamed tissue, indicating loss of overall complexity of the cells in the tissue, the tissue uniformity and structure regularity. Fractal dimension, angular second moment and correlation could be useful methods for quantification of structural changes in acute inflammation.

Neuroimaging as a New Diagnostic Modality in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is characterized by progressive degeneration of upper and lower motor neurons, with variable involvement of extramotor brain regions. Currently, there are no established objective markers of upper motor neuron and extramotor involvement in ALS. Here, we review the potential diagnostic value of advanced neuroimaging techniques that are increasingly being used to study the brain in ALS. First, we discuss the role of different imaging modalities in our increasing understanding of ALS pathogenesis, and their potential to contribute to objective upper motor neuron biomarkers for the disease. Second, we discuss the challenges to be overcome and the required phases of diagnostic test development to translate imaging technology to clinical care. We also present examples of multidimensional imaging approaches to achieve high levels of diagnostic accuracy. Last, we address the role of neuroimaging in clinical therapeutic trials. Advanced neuroimaging techniques will continue to develop and offer significant opportunities to facilitate the development of new effective treatments for ALS.

Fractals in the Neurosciences, Part II

It has been ascertained that the human brain is a complex system studied at multiple scales, from neurons and microcircuits to macronetworks. The brain is characterized by a hierarchical organization that gives rise to its highly topological and functional complexity. Over the last decades, fractal geometry has been shown as a universal tool for the analysis and quantification of the geometric complexity of natural objects, including the brain. The fractal dimension has been identified as a quantitative parameter for the evaluation of the roughness of neural structures, the estimation of time series, and the description of patterns, thus able to discriminate different states of the brain in its entire physiopathological spectrum. Fractal-based computational analyses have been applied to the neurosciences, particularly in the field of clinical neurosciences including neuroimaging and neuroradiology, neurology and neurosurgery, psychiatry and psychology, and neuro-oncology and neuropathology. After a review of the basic concepts of fractal analysis and its main applications to the basic neurosciences in part I of this series, here, we review the main applications of fractals to the clinical neurosciences for a holistic approach towards a fractal geometry model of the brain.

Side of limb-onset predicts laterality of gray matter loss in amyotrophic lateral sclerosis

Conflicting findings have been reported regarding the lateralized brain abnormality in patients with amyotrophic lateral sclerosis (ALS). In this study, we aimed to investigate the probable lateralization of gray matter (GM) atrophy in ALS patients. We focused on the relationship between the asymmetry in decreased GM volume and the side of disease onset in patients with limb-onset. Structural imaging evaluation of normalized atrophy (SIENAX) and voxel-based morphometry (VBM) were used to assess differences in global and local brain regions in patients with heterogeneous body onset and subgroups with different side of limb-onset. We found global brain atrophy and GM losses in the frontal and parietal areas in each patient group as well as left predominant GM losses in the total cohort. The intriguing findings in subgroup analyses demonstrated that the motor cortex in the contralateral hemisphere of the initially involved limb was most affected. We also found that regional brain atrophy was related to disease progression rate. Our observations suggested that side of limb-onset can predict laterality of GM loss in ALS patients and disease progression correlates with the extent of cortical abnormality.