Just out in Neurology: Neuroimmunology & Neuroinflammation!

A new study from our lab shows that long-term cognitive outcomes in patients with anti-LGI1 encephalitis (LGI1-E) are linked to changes in the brain’s white matter network.

Background:

Patients with LGI1-E typically develop a combination of symptoms including seizures, psychiatric symptoms, and cognitive impairment. While most patients respond well to immunotherapy with regard to seizures and psychiatric symptoms, cognitive deficits often persist for many years after the acute illness. However, our understanding of why this is the case remains incomplete.

What our study adds:

We found that patients with LGI1-E show a reorganization of the brain’s white matter network. Overall, networks in patients were less connected than those of age- and sex-matched healthy individuals. Additionally, we observed changes in how ‘central’ individual brain regions are in the white matter networks of patients. To quantify these changes, our work introduces a new measure of network reorganization: the topology deviation index (TDI). Notably, this TDI measure was linked to cognitive outcomes, where patients with a higher degree of network reorganization showed lower performance across several cognitive functions.

What we take away from this:

Although LGI1-E is traditionally viewed as a form of ‘limbic’ encephalitis, our study suggests it should rather be viewed as a ‘network disorder’ that affects the whole brain. Long-term cognitive deficits and the associated reorganization of white matter networks were not prevented by the applied immunotherapy, highlighting the need for closer clinical monitoring and improved treatment strategies to mitigate cognitive impairment in LGI1-E.

For more, check out the paper here: [pdf]

Figure 4. The Extent of White Matter Reorganization Is Associated With Multidomain Cognitive Impairment in Patients With Anti-LGI1 Encephalitis
(A) Reference distribution of betweenness centrality (BC), computed as the median BC value across all 25 healthy control (HC) participants for every brain region. (B) Illustration of the topology deviation index (TDI), computed as the nonparametric correlation distance between the BC distribution of an individual participant (y-axis) and the reference distribution from panel (A) (x-axis). The left plot illustrates a control participant with low topological deviation and the right plot a patient with high deviation. (C) Patients with anti-LGI1 encephalitis show higher TDI scores than HCs (t test, n = 25 each group, d: effect size). (D) Higher TDI scores in patients are associated with impaired cognitive performance across multiple cognitive domains (all p_FDR < 0.02).

We are proud to share the recent publication of our review, MRI Findings in Autoimmune Encephalitis, in the journal Revue Neurologique. This comprehensive review explores how MRI imaging can identify disease-specific patterns across various types of autoimmune encephalitis (AE), including NMDA receptor, LGI1, and GABA receptor-related subtypes.

Highlighting unique imaging features—such as medial temporal lobe hyperintensities in limbic encephalitis or white matter abnormalities in overlap syndromes—the paper emphasizes the role of MRI as a powerful diagnostic and prognostic tool. It also discusses how novel imaging biomarkers and MRI protocols tailored to AE may enhance future diagnostic accuracy, better predict long-term outcomes, and improve long-term patient care.

Our review underscores the importance of advanced imaging techniques for improving diagnosis, tracking disease progression, and refining treatment strategies in AE. The full study is available in Revue Neurologique here.

Our paper on the German psychometric and normative data of the Multifactorial Memory Questionnaire (MMQ) is finally published in Journal of Neurology [link to paper] . It’s completely open access (paper, data, analyses) and the questionnaire freely available for researchers and clinicians at online and can be downloaded here:  [link to questionnaire]

Next to a comprehensive examination of the MMQ scales’ distribution, validity, and reliability in 336 participants with a follow-up after 8 months, we provide reliable change scores, normative data for both the validated 3-factor and 4-factor models, and practical recommendations for their application and interpretation.

Moreover, we show that memory satisfaction and self-rated ability are already decreased at subclinically elevated levels of anxiety, and depressiveness, and that neuropsychiatric factors have a differential impact on metamemory across the lifespan.

New Study Identifies Key Predictors of Post-COVID Fatigue and Cognitive Trajectories

Our latest research, published in the Lancet’s eClinicalMedicine, offers new insights into the long-term trajectories of post-COVID fatigue and cognitive deficits. The study marks a significant step forward in understanding the recovery process from these long-lasting symptoms.

Utilizing data from the COVIDOM / NAPKON-POP cohort in Germany, our team tracked the health outcomes of 3,038 individuals from 6 to over 18 months post-COVID infection. Initial assessments showed that 21% of participants suffered from clinically relevant fatigue and 23% experienced cognitive deficits. Encouragingly, our follow-up revealed considerable improvements, with about half of the patients recovering over time.

Our findings also pinpoint several key predictors for the likelihood of recovery. For fatigue, a higher burden of depressive symptoms and headaches at baseline were linked to reduced recovery chances. Regarding cognitive deficits, general sociodemographic factors (being male, of older age, and having less than 12 years of education), but no COVID-specific factors emerged as significant predictors of non-recovery.

This research not only provides important data for public health care planning, but also lays the groundwork for targeted treatment and prevention.

You can read the paper here [PDF]

In our cohort study, we explored the stability of connectome-based identification in patients with multiple sclerosis (MS) and healthy individuals over extended follow-up periods, up to 4 and 9 years, respectively. Functional connectome fingerprinting, a method for identifying individuals based on their functional connectome, was employed.

Resting-state fMRI data from 70 MS patients and 273 healthy individuals were analyzed. The study revealed that MS patients exhibited connectome stability and identification accuracies comparable to those of healthy individuals. Longer time intervals between imaging sessions correlated with a drop in identification accuracies from 89% to 76%. Interestingly, factors such as lesion load, brain atrophy, or cognitive impairment did not significantly impact identification accuracies within the studied range of disease severity.

Distinctive connections from the fronto-parietal and default mode network consistently played a crucial role in individual identification. Importantly, the functional connectivity patterns also enabled the prediction of individual cognitive performances.

In conclusion, our findings demonstrate the stability of discriminatory signatures in the functional connectome over extended periods in MS patients. This stability results in similar identification accuracies and enduring functional connectome fingerprinting signatures, both in patients and healthy individuals.

You can read the paper here [pdf]

In our study published in Molecular Psychiatry, we delved into the pathophysiology of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis by utilizing patient-derived monoclonal antibodies against the NR1 subunit of the NMDAR in experimental murine models.

Two groups of mice were involved in our investigation: adults injected with human NR1 antibodies and a new model with in utero exposure to these antibodies. Both groups exhibited impaired functional connectivity within the hippocampus, akin to patterns observed in human NMDAR encephalitis patients.

This research establishes a crucial link between human and murine systems, facilitating translational studies to uncover the biological mechanisms of NMDAR encephalitis. The method’s application, alongside imaging and histopathological analyses, holds potential for non-invasive monitoring of disease activity and treatment responses in autoimmune encephalitis. Our work contributes to a better understanding of the condition and may guide future therapeutic approaches.

Read the paper here [pdf]

Source: Kuchling J, Jurek B, Kents M, Kreye J, Geis C, Wickel J, Mueller S, Koch SP, Boehm-Sturm P, Prüss H, Finke C. Impaired functional connectivity of the hippocampus in translational murine models of NMDA-receptor antibody associated neuropsychiatric pathology. Mol Psychiatry. 2023 Oct 24.

In our article published in Behavior Research Methods, we describe the virtual environments navigation assessment (VIENNA), a brief, intuitive, and easy-to apply paradigm, which is specifically designed to evaluate spatial navigation in older adults and clinical populations.

Importantly, VIENNA does not require participants to use an interface device and does not include an episodic memory task. VIENNA was developed using the open source software package PsychoPy and is freely available for researchers and clinicians at www.osf.io/kp4c5.

In our study with 79 healthy participants aged 50 to 85 years, we assessed VIENNA performance and compared it to neuropsychological tests of different domains and self-report measures. We demonstrate the construct validity of VIENNA with a focus on visuospatial and executive functions, its feasibility and favorable psychometric properties, and its sensitivity to age and subjective sense of direction.

For more information, check it out here.

Our review highlights the increasing incidence of sport-related concussion (SRC) and its potential impact on long-term cognitive functioning:

In this study, we uncover a spatiotemporal complexity architecture of spontaneous brain activity – a human “complexome” that is tightly linked to the brain’s organization as a large-scale network.

The key observation is that the brain operates in distinct states of complexity. Specifically, we found that brain activity is highly irregular most of the time, which is reflected by a higher degree of complexity. However, this “default state” is repeatedly interrupted by spontaneous episodes of low complexity (“complexity drops”), in which brain activity becomes much more regular for a short moment in time.

These dynamics offer an explanation for why brain areas interact in functional networks and how activity patterns spread across the brain over time. Furthermore, the approach explains how brain networks change on the scale of seconds to minutes and creates a link between functional and anatomical properties of brain regions. Moreover, our work also shows that neural complexity is linked to age as well as cognitive and motor performance.

For more, feel free to reach out to Stephan and check out the paper here:

https://www.science.org/doi/10.1126/sciadv.abq3851

Charité press release:

https://www.charite.de/forschung/neues_aus_der_forschung/2023/neue_erkenntnisse_zu_hirnnetzwerken/

Twitter thread:

https://twitter.com/krohn_stephan/status/1621527031627517952