Hypothalamic structural integrity and temporal complexity of cortical information processing at rest in migraine without aura patients between attacks

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Objectives

An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.

Materials

Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).

Methods

EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.

Results

The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.

Discussion

FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.

Conclusion

Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.

Salivary Testosterone Levels and Pain Perception Exhibit Sex-Specific Association in Healthy Adults But Not in Patients With Migraine

This study investigated the sex-specific associations between pain perception and testosterone levels in healthy controls (HCs) and patients with migraine. Male and female HCs and migraine patients were recruited. A series of questionnaires were completed by the participants to evaluate their psychosocial profiles, which included data on mood, stress, and sleep quality. Heat pain thresholds and suprathreshold pain ratings at 45 °C (referred to as the pain perception score [PPS]) were assessed using the Thermode system. Salivary testosterone levels were analyzed using a commercial enzyme-linked immunosorbent assay kit. A total of 88 HCs (men/women: 41/47, age: 29.9 ± 7.7 years) and 75 migraine patients (men/women: 30/45, age: 31.1 ± 7.7 years) completed all assessments. No significant differences were observed in either the psychosocial profiles or heat pain thresholds and PPSs between the sexes in the control and migraine groups. A positive correlation between testosterone levels and PPSs was identified in the male controls (r = .341, P = .029), whereas a negative correlation was identified in the female controls (r = -.407, P = .005). No such correlations were identified in the migraine group. This study confirms that a negative association is present between PPSs and testosterone levels in female controls, which is in line with the findings that testosterone is associated with reduced pain perception. Our study is the first to demonstrate a sex-specific association between PPSs and testosterone levels in HCs. Moreover, this study also revealed that the presence of migraine appears to disrupt this association. PERSPECTIVE: This study revealed that testosterone levels demonstrate opposite associations with pain perception in healthy men and women. However, the presence of migraine appears to disrupt this sex-specific association.

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

BACKGROUND

Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline.

OBJECTIVES

The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features.

METHODS

Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models.

RESULTS

FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity.

CONCLUSIONS

Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fractal Time Series: Background, Estimation Methods, and Performances

Over the past 40 years, from its classical application in the characterization of geometrical objects, fractal analysis has been progressively applied to study time series in several different disciplines. In neuroscience, starting from identifying the fractal properties of neuronal and brain architecture, attention has shifted to evaluating brain signals in the time domain. Classical linear methods applied to analyzing neurophysiological signals can lead to classifying irregular components as noise, with a potential loss of information. Thus, characterizing fractal properties, namely, self-similarity, scale invariance, and fractal dimension (FD), can provide relevant information on these signals in physiological and pathological conditions. Several methods have been proposed to estimate the fractal properties of these neurophysiological signals. However, the effects of signal characteristics (e.g., its stationarity) and other signal parameters, such as sampling frequency, amplitude, and noise level, have partially been tested. In this chapter, we first outline the main properties of fractals in the domain of space (fractal geometry) and time (fractal time series). Then, after providing an overview of the available methods to estimate the FD, we test them on synthetic time series (STS) with different sampling frequencies, signal amplitudes, and noise levels. Finally, we describe and discuss the performances of each method and the effect of signal parameters on the accuracy of FD estimation.

Insights into migraine attacks from neuroimaging

Migraine is one of the most common neurological diseases and it has a huge social and personal impact. Although head pain is the core symptom, individuals with migraine can have a plethora of non-headache symptoms that precede, accompany, or follow the pain. Neuroimaging studies have shown that the involvement of specific brain areas can explain many of the symptoms reported during the different phases of migraine. Recruitment of the hypothalamus, pons, spinal trigeminal nucleus, thalamus, and visual and pain-processing cortical areas starts during the premonitory phase and persists through the headache phase, contributing to the onset of pain and associated symptoms. Once the pain stops, the involvement of most brain areas ends, although the pons, hypothalamus, and visual cortex remain active after acute treatment intake and resolution of migraine symptoms. A better understanding of the correlations between imaging findings and migraine symptomatology can provide new insight into migraine pathophysiology and the mechanisms of novel migraine-specific treatments.

Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability

OBJECTIVE

The changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine.

METHODS

Twenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated.

RESULTS

Compared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained.

CONCLUSIONS

Our study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.

A Hypothalamic Mechanism Regulates the Duration of a Migraine Attack: Insights from Microstructural and Temporal Complexity of Cortical Functional Networks Analysis

The role of the hypothalamus and the limbic system at the onset of a migraine attack has recently received significant interest. We analyzed diffusion tensor imaging (DTI) parameters of the entire hypothalamus and its subregions in 15 patients during a spontaneous migraine attack and in 20 control subjects. We also estimated the non-linear measure resting-state functional MRI BOLD signal's complexity using Higuchi fractal dimension (FD) and correlated DTI/fMRI findings with patients' clinical characteristics. In comparison with healthy controls, patients had significantly altered diffusivity metrics within the hypothalamus, mainly in posterior ROIs, and higher FD values in the salience network (SN). We observed a positive correlation of the hypothalamic axial diffusivity with migraine severity and FD of SN. DTI metrics of bilateral anterior hypothalamus positively correlated with the mean attack duration. Our results show plastic structural changes in the hypothalamus related to the attacks severity and the functional connectivity of the SN involved in the multidimensional neurocognitive processing of pain. Plastic changes to the hypothalamus may play a role in modulating the duration of the attack.

The Patent Foramen Ovale and Migraine: Associated Mechanisms and Perspectives from MRI Evidence

Migraine is a common neurological disease with a still-unclear etiology and pathogenesis. Patent foramen ovale (PFO) is a kind of congenital heart disease that leads to a right-to-left shunt (RLS). Although previous studies have shown that PFO has an effect on migraine, a clear conclusion about the link between PFO and migraine is lacking. We first summarized the PFO potential mechanisms associated with migraine, including microembolus-triggered cortical spreading depression (CSD), the vasoactive substance hypothesis, impaired cerebral autoregulation (CA), and a common genetic basis. Further, we analyzed the changes in brain structure and function in migraine patients and migraine patients with PFO. We found that in migraine patients with PFO, the presence of PFO may affect the structure of the cerebral cortex and the integrity of white matter, which is mainly locked in subcortical, deep white matter, and posterior circulation, and may lead to changes in brain function, such as cerebellum and colliculus, which are involved in the processing and transmission of pain. In summary, this paper provides neuroimaging evidence and new insights into the correlation between PFO and migraine, which will help to clarify the etiology and pathogenesis of migraine, and aid in the diagnosis and treatment of migraine in the future.

Cerebro-Cerebellar Networks in Migraine Symptoms and Headache

The cerebellum is associated with the biology of migraine in a variety of ways. Clinically, symptoms such as fatigue, motor weakness, vertigo, dizziness, difficulty concentrating and finding words, nausea, and visual disturbances are common in different types of migraine. The neural basis of these symptoms is complex, not completely known, and likely involve activation of both specific and shared circuits throughout the brain. Posterior circulation stroke, or neurosurgical removal of posterior fossa tumors, as well as anatomical tract tracing in animals, provided the first insights to theorize about cerebellar functions. Nowadays, with the addition of functional imaging, much progress has been done on cerebellar structure and function in health and disease, and, as a consequence, the theories refined. Accordingly, the cerebellum may be useful but not necessary for the execution of motor, sensory or cognitive tasks, but, rather, would participate as an efficiency facilitator of neurologic functions by improving speed and skill in performance of tasks produced by the cerebral area to which it is reciprocally connected. At the subcortical level, critical regions in these processes are the basal ganglia and thalamic nuclei. Altogether, a modulatory role of the cerebellum over multiple brain regions appears compelling, mainly by considering the complexity of its reciprocal connections to common neural networks involved in motor, vestibular, cognitive, affective, sensory, and autonomic processing—all functions affected at different phases and degrees across the migraine spectrum. Despite the many associations between cerebellum and migraine, it is not known whether this structure contributes to migraine initiation, symptoms generation or headache. Specific cerebellar dysfunction via genetically driven excitatory/inhibitory imbalances, oligemia and/or increased risk to white matter lesions has been proposed as a critical contributor to migraine pathogenesis. Therefore, given that neural projections and functions of many brainstem, midbrain and forebrain areas are shared between the cerebellum and migraine trigeminovascular pathways, this review will provide a synopsis on cerebellar structure and function, its role in trigeminal pain, and an updated overview of relevant clinical and preclinical literature on the potential role of cerebellar networks in migraine pathophysiology.

Characterizing Fractal Genetic Variation in the Human Genome from the Hapmap Project

Over the last decades, the exuberant development of next-generation sequencing has revolutionized gene discovery. These technologies have boosted the mapping of single nucleotide polymorphisms (SNPs) across the human genome, providing a complex universe of heterogeneity characterizing individuals worldwide. Fractal dimension (FD) measures the degree of geometric irregularity, quantifying how "complex" a self-similar natural phenomenon is. We compared two FD algorithms, box-counting dimension (BCD) and Higuchi's fractal dimension (HFD), to characterize genome-wide patterns of SNPs extracted from the HapMap data set, which includes data from 1184 healthy subjects of eleven populations. In addition, we have used cluster and classification analysis to relate the genetic distances within chromosomes based on FD similarities to the geographical distances among the 11 global populations. We found that HFD outperformed BCD at both grand average clusterization analysis by the cophenetic correlation coefficient, in which the closest value to 1 represents the most accurate clustering solution (0.981 for the HFD and 0.956 for the BCD) and classification (79.0% accuracy, 61.7% sensitivity, and 96.4% specificity for the HFD with respect to 69.1% accuracy, 43.2% sensitivity, and 94.9% specificity for the BCD) of the 11 populations present in the HapMap data set. These results support the evidence that HFD is a reliable measure helpful in representing individual variations within all chromosomes and categorizing individuals and global populations.