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.

Clinical Sensitivity of Fractal Neurodynamics.

Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.

Fractal Dimension as a discriminative feature for high accuracy classification in motor imagery EEG-based brain-computer interface.

BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. METHODS: In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). RESULTS: Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. CONCLUSIONS: These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.

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.

Effective Intracerebral Connectivity in Acute Stroke: A TMS-EEG Study.

Stroke is a major cause of disability because of its motor and cognitive sequelae even when the acute phase of stabilization of vital parameters is overcome. The most important improvements occur in the first 8-12 weeks after stroke, indicating that it is crucial to improve our understanding of the dynamics of phenomena occurring in this time window to prospectively target rehabilitation procedures from the earliest stages after the event. Here, we studied the intracortical excitability properties of delivering transcranial magnetic stimulation (TMS) to the primary motor cortex (M1) of left and right hemispheres in 17 stroke patients who suffered a mono-lateral left hemispheric stroke, excluding pure cortical damage. All patients were studied within 10 days of symptom onset. TMS-evoked potentials (TEPs) were collected via a TMS-compatible electroencephalogram system (TMS-EEG) concurrently with motor-evoked responses (MEPs) induced in the contralateral first dorsal interosseous muscle. Comparison with age-matched healthy volunteers was made by collecting the same bilateral-stimulation data in nine healthy volunteers as controls. Excitability in the acute phase revealed relevant changes in the relationship between left lesioned and contralesionally right hemispheric homologous areas both for TEPs and MEPs. While the paretic hand displayed reduced MEPs compared to the non-paretic hand and to healthy volunteers, TEPs revealed an overexcitable lesioned hemisphere with respect to both healthy volunteers and the contra-lesion side. Our quantitative results advance the understanding of the impairment of intracortical inhibitory networks. The neuronal dysfunction most probably changes the excitatory/inhibitory on-center off-surround organization that supports already acquired learning and reorganization phenomena that support recovery from stroke sequelae.

Combining Transcranial Magnetic Stimulation and Deep Brain Stimulation: Current Knowledge, Relevance and Future Perspectives.

Deep brain stimulation (DBS) has emerged as an invasive neuromodulation technique for the treatment of several neurological disorders, but the mechanisms underlying its effects remain partially elusive. In this context, the application of Transcranial Magnetic Stimulation (TMS) in patients treated with DBS represents an intriguing approach to investigate the neurophysiology of cortico-basal networks. Experimental studies combining TMS and DBS that have been performed so far have mainly aimed to evaluate the effects of DBS on the cerebral cortex and thus to provide insights into DBS's mechanisms of action. The modulation of cortical excitability and plasticity by DBS is emerging as a potential contributor to its therapeutic effects. Moreover, pairing DBS and TMS stimuli could represent a method to induce cortical synaptic plasticity, the therapeutic potential of which is still unexplored. Furthermore, the advent of new DBS technologies and novel treatment targets will present new research opportunities and prospects to investigate brain networks. However, the application of the combined TMS-DBS approach is currently limited by safety concerns. In this review, we sought to present an overview of studies performed by combining TMS and DBS in neurological disorders, as well as available evidence and recommendations on the safety of their combination. Additionally, we outline perspectives for future research by highlighting knowledge gaps and possible novel applications of this approach.

Functional balance at rest of hemispheric homologs assessed via normalized compression distance.

Introduction: The formation and functioning of neural networks hinge critically on the balance between structurally homologous areas in the hemispheres. This balance, reflecting their physiological relationship, is fundamental for learning processes. In our study, we explore this functional homology in the resting state, employing a complexity measure that accounts for the temporal patterns in neurodynamics. Methods: We used Normalized Compression Distance (NCD) to assess the similarity over time, neurodynamics, of the somatosensory areas associated with hand perception (S1). This assessment was conducted using magnetoencephalography (MEG) in conjunction with Functional Source Separation (FSS). Our primary hypothesis posited that neurodynamic similarity would be more pronounced within individual subjects than across different individuals. Additionally, we investigated whether this similarity is influenced by hemisphere or age at a population level. Results: Our findings validate the hypothesis, indicating that NCD is a robust tool for capturing balanced functional homology between hemispheric regions. Notably, we observed a higher degree of neurodynamic similarity in the population within the left hemisphere compared to the right. Also, we found that intra-subject functional homology displayed greater variability in older individuals than in younger ones. Discussion: Our approach could be instrumental in investigating chronic neurological conditions marked by imbalances in brain activity, such as depression, addiction, fatigue, and epilepsy. It holds potential for aiding in the development of new therapeutic strategies tailored to these complex conditions, though further research is needed to fully realize this potential.

A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.

Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.

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.

Fractal Dimension Feature as a Signature of Severity in Disorders of Consciousness: An EEG Study.

An accurate diagnosis of the disorder of consciousness (DOC) is essential for generating tailored treatment programs. Accurately diagnosing patients with a vegetative state (VS) and patients in a minimally conscious state (MCS), however, might be very complicated, reaching a misdiagnosis of approximately 40% if clinical scales are not carefully administered and continuously repeated. To improve diagnostic accuracy for those patients, tools such as electroencephalography (EEG) might be used in the clinical setting. Many linear indices have been developed to improve the diagnosis in DOC patients, such as spectral power in different EEG frequency bands, spectral power ratios between these bands, and the difference between eyes-closed and eyes-open conditions (i.e. alpha-blocking). On the other hand, much less has been explored using nonlinear approaches. Therefore, in this work, we aim to discriminate between MCS and VS groups using a nonlinear method called Higuchi's Fractal Dimension (HFD) and show that HFD is more sensitive than linear methods based on spectral power methods. For the sake of completeness, HFD has also been tested against another nonlinear approach widely used in EEG research, the Entropy (E). To our knowledge, this is the first time that HFD has been used in EEG data at rest to discriminate between MCS and VS patients. A comparison of Bayes factors found that differences between MCS and VS were 11 times more likely to be detected using HFD than the best performing linear method tested and almost 32 times with respect to the E. Machine learning has also been tested for HFD, reaching an accuracy of 88.6% in discriminating among VS, MCS and healthy controls. Furthermore, correlation analysis showed that HFD was more robust to outliers than spectral power methods, showing a clear positive correlation between the HFD and Coma Recovery Scale-Revised (CRS-R) values. In conclusion, our work suggests that HFD could be used as a sensitive marker to discriminate between MCS and VS patients and help decrease misdiagnosis in clinical practice when combined with commonly used clinical scales.

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging.

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

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.

Dynamics of the "Cognitive" Brain Wave P3b at Rest for Alzheimer Dementia Prediction in Mild Cognitive Impairment.

Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.

Corticomuscular Coherence Dependence on Body Side and Visual Feedback.

Physiological movement develops on the basis of sensorimotor integration through synchronisation between the copy of signals sent to the effector muscles and the incoming flow of sensory information. Our aim is to study corticomuscular coherence (CMC), the most widely used measure of synchronization between brain and muscle electrical activities, in dependence on the level of visual feedback and the executing body side. We analysed CMC in 18 healthy volunteers while performing a weak isometric handgrip of an air bulb with either the right or the left hand, in either the presence or absence of visual feedback on the exerted pressure. The absence of visual feedback decreased the CMC peak frequency from 27 Hz to 23 Hz (p < 0.001), increased the CMC peak amplitude from 0.05 to 0.07 (p = 0.005) and decreased the electroencephalographic beta band power (p = 0.005). None of these measures changed in dependence on the performing hand (p > 0.2 consistently). The lack of dependence of CMC on the controlled hand involved in the movement can be considered in agreement with small hemispheric asymmetries of hand representations in primary sensorimotor cortices. Modulation of visual information changed corticomuscular synchronizations and cortical involvement, reflecting the crucial role of gaze in human behaviour. Given the fundamental role of sensory integration in motor execution, the availability of a simple index sensitive to modulations of perceptual afferents may prove useful in determining the use or the monitoring of the effects of sensory enrichments in personalized rehabilitation.

Bimodal sensory integration in migraine: A study of the effect of visual stimulation on somatosensory evoked cortical responses.

BACKGROUND: Merging of sensory information is a crucial process for adapting the behaviour to the environment in all species. It is not known if this multisensory integration might be dysfunctioning interictally in migraine without aura, where sensory stimuli of various modalities are processed abnormally when delivered separately. To investigate this question, we compared the effects of a concomitant visual stimulation on conventional low-frequency somatosensory evoked potentials and embedded high-frequency oscillations between migraine patients and healthy volunteers. METHODS: We recorded somatosensory evoked potentials in 19 healthy volunteers and in 19 interictal migraine without aura patients before, during, and 5 min after (T2) simultaneous synchronous pattern-reversal visual stimulation. At each time point, we measured amplitude and habituation of the N20-P25 low-frequency-somatosensory evoked potentials component and maximal peak-to-peak amplitude of early and late bursts of high-frequency oscillations. RESULTS: In healthy volunteers, the bimodal stimulation significantly reduced low-frequency-somatosensory evoked potentials habituation and tended to reduce early high-frequency oscillations that reflect thalamocortical activity. By contrast, in migraine without aura patients, bimodal stimulation significantly increased low-frequency-somatosensory evoked potentials habituation and early high-frequency oscillations. At T2, all visual stimulation-induced changes of somatosensory processing had vanished. CONCLUSION: These results suggest a malfunctioning multisensory integration process, which could be favoured by an abnormal excitability level of thalamo-cortical loops.

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

The hypothalamus has been attributed an important role during the premonitory phase of a migraine attack. Less is known about the role played by the hypothalamus in the interictal period and its relationship with the putative neurocognitive networks previously identified in the pathophysiology of migraine. Our aim was to test whether the hypothalamic microstructure would be altered during the interictal period and whether this co-existed with aberrant connectivity at cortical level. We collected multimodal MRI data from 20 untreated patients with migraine without aura between attacks (MO) and 20 healthy controls (HC) and studied fractional anisotropy, mean (MD), radial (RD), and axial diffusivity of the hypothalamus ROI as a whole from diffusion tensor imaging (DTI). Moreover, we performed an exploratory analysis of the same DTI metrics separately for the anterior and posterior hypothalamic ROIs bilaterally. From resting-state functional MRI, we estimated the Higuchi's fractal dimension (FD), an index of temporal complexity sensible to describe non-periodic patterns characterizing BOLD signature. Finally, we correlated neuroimaging findings with migraine clinical features. In comparison to HC, MO had significantly higher MD, AD, and RD values within the hypothalamus. These findings were confirmed also in the exploratory analysis on the sub-regions of the hypothalamus bilaterally, with the addition of lower FA values on the posterior ROIs. Patients showed higher FD values within the salience network (SN) and the cerebellum, and lower FD values within the primary visual (PV) network compared to HC. We found a positive correlation between cerebellar and SN FD values and severity of migraine. Our findings of hypothalamic abnormalities between migraine attacks may form part of the neuroanatomical substrate that predisposes the onset of the prodromal phase and, therefore, the initiation of an attack. The peculiar fractal dimensionality we found in PV, SN, and cerebellum may be interpreted as an expression of abnormal efficiency demand of brain networks devoted to the integration of sensory, emotional, and cognitive information related to the severity of migraine.

The Timecourse of Electrophysiological Brain-Heart Interaction in DoC Patients.

Disorders of Consciousness (DOC) are a spectrum of pathologies affecting one's ability to interact with the external world. Two possible conditions of patients with DOC are Unresponsive Wakefulness Syndrome/Vegetative State (UWS/VS) and Minimally Conscious State (MCS). Analysis of spontaneous EEG activity and the Heart Rate Variability (HRV) are effective techniques in exploring and evaluating patients with DOC. This study aims to observe fluctuations in EEG and HRV parameters in the morning/afternoon resting-state recording. The study enrolled 13 voluntary Healthy Control (HC) subjects and 12 DOC patients (7 MCS, 5 UWS/VS). EEG and EKG were recorded. PSDalpha, PSDtheta powerband, alpha-blocking, alpha/theta of the EEG, Complexity Index (CI) and SDNN of EKG were analyzed. Higher values of PSDalpha, alpha-blocking, alpha/theta and CI values and lower values of PSD theta characterized HC individuals in the morning with respect to DOC patients. In the afternoon, we detected a significant difference between groups in the CI, PSDalpha, PSDtheta, alpha/theta and SDNN, with lower PSDtheta value for HC. CRS-R scores showed a strong correlation with recorded parameters mainly during evaluations in the morning. Our finding put in evidence the importance of the assessment, as the stimulation of DOC patients in research for behavioural response, in the morning.

Thalamo-cortical networks in subtypes of migraine with aura patients.

BACKGROUND: We searched for differences in resting-state functional connectivity (FC) between brain networks and its relationship with the microstructure of the thalamus between migraine with pure visual auras (MA), and migraine with complex neurological auras (MA+), i.e. with the addition of at least one of sensory or language symptom. METHODS: 3T MRI data were obtained from 20 patients with MA and 15 with MA + and compared with those from 19 healthy controls (HCs). We collected resting state data among independent component networks. Diffusivity metrics of bilateral thalami were calculated and correlated with resting state ICs-Z-scores. RESULTS: As compared to HCs, both patients with MA and MA + disclosed disrupted FC between the default mode network (DMN) and the right dorsal attention system (DAS). The MA + subgroup had lower microstructural metrics than both HCs and the MA subgroup, which correlated negatively with the strength of DMN connectivity. Although the microstructural metrics of MA patients did not differ from those of HCs, these patients lacked the correlation with the strength of DAS connectivity found in HCs. CONCLUSIONS: The present findings suggest that, as far as MRI profiles are concerned, the two clinical phenotypes of migraine with aura have both common and distinct morpho-functional features of nodes in the thalamo-cortical network.