Spectral signatures of mirror movements in the sensori-motor connectivity in kallmann syndrome

Dynamic Functional Network Connectivity in Acute Incomplete Cervical Cord Injury Patients and Its Associations With Sensorimotor Dysfunction Measures

BACKGROUND

Dynamic functional network connectivity (dFNC) captures temporal variations in functional connectivity during magnetic resonance imaging acquisition. However, the neural mechanisms driving dFNC alterations in the brain networks of patients with acute incomplete cervical cord injury (AICCI) remain unclear.

METHODS

This study included 16 AICCI patients and 16 healthy controls. Initially, independent component analysis was employed to extract whole-brain independent components from resting-state functional magnetic resonance imaging data. Subsequently, a sliding time window approach, combined with k-means clustering, was used to estimate dFNC states for each participant. Finally, a correlation analysis was conducted to examine the association between sensorimotor dysfunction scores in AICCI patients and the temporal characteristics of dFNC.

RESULTS

Independent component analysis was employed to extract 26 whole-brain independent components. Subsequent dynamic analysis identified 4 distinct connectivity states across the entire cohort. Notably, AICCI patients demonstrated a significant preference for State 3 compared to healthy controls, as evidenced by a higher frequency and longer duration spent in this state. Conversely, State 4 exhibited a reduced frequency and shorter dwell time in AICCI patients. Moreover, correlation analysis revealed a positive association between sensorimotor dysfunction and both the mean dwell time and the fraction of time spent in State 3.

CONCLUSIONS

Patients with AICCI demonstrate abnormal connectivity within dFNC states, and the temporal characteristics of dFNC are associated with sensorimotor dysfunction scores. These findings highlight the potential of dFNC as a sensitive biomarker for detecting network functional changes in AICCI patients, providing valuable insights into the dynamic alterations in brain connectivity related to sensorimotor dysfunction in this population.

A Reflection on Motor Overflow, Mirror Phenomena, Synkinesia and Entrainment

In patients with movement disorders, voluntary movements can sometimes be accompanied by unintentional muscle contractions in other body regions. In this review, we discuss clinical and pathophysiological aspects of several motor phenomena including mirror movements, dystonic overflow, synkinesia, entrainment and mirror dystonia, focusing on their similarities and differences. These phenomena share some common clinical and pathophysiological features, which often leads to confusion in their definition. However, they differ in several aspects, such as the body part showing the undesired movement, the type of this movement (identical or not to the intentional movement), the underlying neurological condition, and the role of primary motor areas, descending pathways and inhibitory circuits involved, suggesting that these are distinct phenomena. We summarize the main features of these fascinating clinical signs aiming to improve the clinical recognition and standardize the terminology in research studies. We also suggest that the term "mirror dystonia" may be not appropriate to describe this peculiar phenomenon which may be closer to dystonic overflow rather than to the classical mirror movements.

Dynamic spectral signatures of mirror movements in the sensorimotor functional connectivity network of patients with Kallmann syndrome

In Kallmann syndrome (KS), the peculiar phenomenon of bimanual synkinesis or mirror movement (MM) has been associated with a spectral shift, from lower to higher frequencies, of the resting-state fMRI signal of the large-scale sensorimotor brain network (SMN). To possibly determine whether a similar frequency specificity exists across different functional connectivity SMN states, and to capture spontaneous transitions between them, we investigated the dynamic spectral changes of the SMN functional connectivity in KS patients with and without MM symptom. Brain MRI data were acquired at 3 Tesla in 39 KS patients (32 without MM, KSMM-, seven with MM, KSMM+) and 26 age- and sex-matched healthy control (HC) individuals. The imaging protocol included 20-min rs-fMRI scans enabling detailed spectro-temporal analyses of large-scale functional connectivity brain networks. Group independent component analysis was used to extract the SMN. A sliding window approach was used to extract the dynamic spectral power of the SMN functional connectivity within the canonical physiological frequency range of slow rs-fMRI signal fluctuations (0.01–0.25 Hz). K-means clustering was used to determine (and count) the most recurrent dynamic states of the SMN and detect the number of transitions between them. Two most recurrent states were identified, for which the spectral power peaked at a relatively lower (state 1) and higher (state 2) frequency. Compared to KS patients without MM and HC subjects, the SMN of KS patients with MM displayed significantly larger spectral power changes in the slow 3 canonical sub-band (0.073–0.198 Hz) and significantly fewer transitions between state 1 (less recurrent) and state 2 (more recurrent). These findings demonstrate that the presence of MM in KS patients is associated with reduced spontaneous transitions of the SMN between dynamic functional connectivity states and a higher recurrence and an increased spectral power change of the high-frequency state. These results provide novel information about the large-scale brain functional dynamics that could help to understand the pathologic mechanisms of bimanual synkinesis in KS syndrome and, potentially, other neurological disorders where MM may also occur.

Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke

Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.

Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency.

Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed.

Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV.

Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

Dynamics of Top-Down Control and Motor Networks in Parkinson's Disease

BACKGROUND

Motor symptoms in Parkinson's disease (PD) patients might be related to high-level task-control deficits. We aimed at investigating the dynamics between sensorimotor network and top-down control networks (frontal-parietal, cingulo-opercular, and cerebellar) in PD and at determining the effects of levodopa on the dynamics of these networks.

METHODS

We investigated dynamic functional connectivity (dFC), during resting state functional magnetic resonance imaging, between sensorimotor network and top-down control networks in 36 PD patients (OFF medication, PD-OFF) and 36 healthy volunteers. We further assessed the effect of medication on dFC in18 PD patients who were also scanned ON medication.

RESULTS

The dFC analyses identified three discrete states: State I (35.68%) characterized by connections between the cerebellum and sensorimotor network, State II (34.17%) with connections between the sensorimotor and frontal-parietal network, and State III (30.15%) with connection between the sensorimotor and cingulo-opercular network. PD patients have significantly fewer occurrences and overall spent less time (shorter dwell time) in State II compared to healthy controls. After levodopa intake, dwell time improved toward normal. The change in dwell time before and after taking levodopa was negatively related to the respective changes in Unified Parkinson's Disease Rating Scale, Part III. PD-OFF showed significantly decreased connectivity between sensorimotor and control networks and increased connectivity within control networks. These changes were partially improved after levodopa intake.

CONCLUSIONS

Dopamine depletion in PD is associated with abnormalities in temporal and spatial properties between cognitive control and sensorimotor network, possibly contributing to clinical deficits. Levodopa partially restores the network function toward the values observed in healthy volunteers. © 2021 International Parkinson and Movement Disorder Society.