Johanna Uusimaa

Purpose: Developing a valid and practical method for obtaining brain tissue samples for transcriptomic and proteomic analyses using tissue adhered to stereoelectroencephalography (SEEG) electrodes, to enable investigation of the molecular mechanisms underlying chronic epilepsy. Method: Brain tissue samples adhered to SEEG electrodes were collected from six patients with drug-resistant epilepsy and preprocessed in a hospital environment after electrode removal. RNA extraction was initiated immediately, and proteomics samples were snapfrozen until subsequent analysis. The samples were categorized into three groups according to their electrophysiological profile: epileptogenic zone, propagation zone, and least-involved zone. The omic findings between these zones and anatomical brain areas were compared. Results: High-quality RNA and protein samples were obtained from tissue adhered to SEEG electrodes. Neuron- and brain-specific gene expression patterns and proteins were identified. Signs of activation of inflammatory mechanisms were most pronounced in the epileptogenic zone. Transcriptomic and proteomic findings demonstrated concordance. Conclusion: SEEG electrodes are a useful source for obtaining brain tissue for molecular characterization of chronic epilepsy. This method will enable the identification of shared and distinct molecular mechanisms in patients with varying etiologies of epilepsy.

BACKGROUND Digital care pathways are increasingly used to support chronic disease management, yet empirical evidence of their impact on treatment burden remains scarce. OBJECTIVE This study examines how a digital care pathway for epilepsy (DCPE) influences treatment burden and what are its effects on daily lives of persons with epilepsy METHODS A qualitative single-case study was conducted in a Finnish healthcare setting. Semistructured interviews were carried out with persons with epilepsy (n=18), caregivers(n=3), and healthcare professionals (n=7). Data were analyzed deductively using the Burden of Treatment Theory, focusing on care-related work, challenges, and impacts on well-being. RESULTS DCPE was perceived as convenient, easy to use, timesaving, and reducing treatment burden for most users. It was particularly suitable for routine communication once care relationship had been established, while face-to-face interactions remained important for newly diagnosed or in treatment change. DCPE supported routine medication management well. However, it did not address broader psychosocial impacts of epilepsy, and further development needs were identified. CONCLUSIONS DCPE can reduce treatment burden and improve access to care for most users, particularly in routine care. However, hybrid care models remain essential, as face-toface support was preferred in certain phases of care pathway. Future development should focus on the diverse needs of people with epilepsy and on addressing the psychosocial aspects of living with epilepsy.

In this paper, we present a scenario-based method for identifying stakeholders’ insights for sustainable digital healthcare by leveraging future digital healthcare scenarios and changing social sustainability drivers. This study aims to propose a scenario-based stakeholder insight method to support the development of inclusive and socially sustainable digital healthcare futures. The first eleven change drivers associated with social sustainability were extracted from the digital healthcare literature and presented to various stakeholders to rank their impact and predictability regarding the social sustainability of digital healthcare. Moreover, the proposed method is also designed to extract insights from stakeholders regarding the implementation of different future scenarios, (i) the consequences of various future scenarios, (ii) the risks and challenges of achieving the future scenarios, and (iii) recommendations for policymakers. The study’s stakeholders include medical doctors, digital healthcare researchers, developers, and digital health-related businesses. The results present the most decisive, impactful, and predictable scenario as “connected care for all,” an inclusive digital healthcare system accessible to all without digital exclusion. Moreover, results show the most impactful and uncertain change driver, as rated by stakeholders. The proposed method can be used to understand stakeholders’ social sustainability requirements, leading to a sustainable digital healthcare future.

Variants of unknown significance (VUS) pose an extensive clinical challenge. Our objective was to explore the diagnostic pipeline from symptom onset to molecular diagnosis in autosomal recessive (Spastic ataxia type 2 [SPAX2], Mendelian Inheritance in Man [MIM] number 611302) caused by a new homozygous variant in the KIF1C gene. Two unrelated individuals with early-onset spastic ataxia were evaluated for genetic etiology by exome sequencing. Case reports were compiled through a medical chart review. Two cellular models were established to assess variant pathogenicity. Whole exome sequencing revealed a homozygous variant in KIF1C (NM_006612.6: c.833T > C, p.[Leu278Pro]) in a highly conserved motor domain of the KIF1C protein in both individuals. Two cellular models overexpressing a green fluorescent protein (GFP)-tagged KIF1C harboring the p.Leu278Pro variant demonstrated disrupted protein localization, suggesting an impaired trafficking capacity of the mutant KIF1C. A diagnosis of SPAX2 was established based on the in vitro data. Novel clinical findings associated with this KIF1C variant included retinal dysfunction detected by electroretinogram, hypotonia, and a thin corpus callosum in brain MRI. Classification of pathogenicity requires extensive multidisciplinary effort, which can be burdensome for affected individuals and families. Like other proteins of the kinesin family, variants in KIF1C may underlie retinal dysfunction.

[This corrects the article DOI: 10.2196/59558.].

UNSTRUCTURED Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients’quality of life. Therefore, it is critical to track epileptic seizures, diagnose them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized ensuring that the proposed system is realistic and achievable. Perspectives on the feasibility of data-driven medical software that meets the project’s goals and demonstrates practical feasibility regarding resource availability, time constraints, and technological capabilities are presented. A 4-round Delphi study using focus group discussions was conducted with 7 diverse panels of experts from Oulu University Hospital to address the research questions and evaluate the feasibility of data-driven medical software for monitoring individuals with epilepsy. This collaborative approach fostered a thorough understanding of the topic and considered the perspectives of various stakeholders. In addition, a qualitative study was carried out using semistructured interviews Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study identified practical strategies, such as real-time EEG seizure monitoring, predictive modeling for seizure occurrence, and data-driven analytics integration to enhance decision-making. These strategies were aimed at reducing diagnostic delays and providing personalized care. We are actively working on integrating these features into clinical workflows. However, further case studies and pilot implementations are planned for future studies. The results of this study will guide system developers in the meticulous design and development of systems that meet user needs in the DCPE.

Background: Abnormal brain activity is the source of epileptic seizures, which can present a variety of symptoms and influence patients’ quality of life. Therefore, it is critical to track epileptic seizures, diagnose them, and provide potential therapies to manage people with epilepsy. Electroencephalography (EEG) is helpful in the diagnosis and classification of the seizure type, epilepsy, or epilepsy syndrome. Ictal EEG is rarely recorded, whereas interictal EEG is more often recorded, and the results can be abnormal or normal even in the case of epilepsy. The current digital care pathway for epilepsy (DCPE) lacks the integration of data-driven seizure detection, which could potentially enhance epilepsy treatment and management. Objective: This study aimed to determine the requirements for integrating data-driven medical software into the DCPE to meet the project’s goals and demonstrate practical feasibility regarding resource availability, time constraints, and technological capabilities. This adjustment emphasized ensuring that the proposed system is realistic and achievable. Perspectives on the feasibility of data-driven medical software that meets the project’s goals and demonstrates practical feasibility regarding resource availability, time constraints, and technological capabilities are presented. Methods: A 4-round Delphi study using focus group discussions was conducted with 7 diverse panels of experts from Oulu University Hospital to address the research questions and evaluate the feasibility of data-driven medical software for monitoring individuals with epilepsy. This collaborative approach fostered a thorough understanding of the topic and considered the perspectives of various stakeholders. In addition, a qualitative study was carried out using semistructured interviews. Results: Drawing from the findings of the thematic analytics, a detailed set of guidelines was created to facilitate the seamless integration of the proposed data-driven medical software for EEG seizure monitoring into the DCPE. These guidelines encompass system requirements, data collection and analysis, and user training, offering a comprehensive road map for the effective implementation of the software. Conclusions: The study outcome presents a comprehensive strategy for improving the quality of care, providing personalized solutions, managing health care resources, and using artificial intelligence and sensor technology in clinical settings. The potential of artificial intelligence and sensor technology to revolutionize health care is exciting. The study identified practical strategies, such as real-time EEG seizure monitoring, predictive modeling for seizure occurrence, and data-driven analytics integration to enhance decision-making. These strategies were aimed at reducing diagnostic delays and providing personalized care. We are actively working on integrating these features into clinical workflows. However, further case studies and pilot implementations are planned for future studies. The results of this study will guide system developers in the meticulous design and development of systems that meet user needs in the DCPE.

Background: The genetic landscape of pediatric cerebellar disorders (PCDs) in Finland is undefined.Objectives: The objective was to define epidemiological, clinical, neuroradiological, and genetic characteristics of PCDs in Northern Finland.Methods: A longitudinal population‐based cohort study of children with a movement disorder or a cerebellar malformation (diagnosis ≤16 years; study period 1970–2022) was performed in the tertiary catchment area of the Oulu University Hospital, Finland. The genotype‐to‐phenotype associations were compared with 1007 published cases with matching monogenic etiologies.Results: A total of 107 patients were included (cumulative incidence 21.9 per 100,000 live births). A defined genetic or non‐genetic etiology was identified for 59 patients. These etiologies were monogenic (66%), chromosomal (12%), or non‐genetic (22%). Ataxia was the most common movement disorder. Friedreich's ataxia was uncommon, whereas ataxias belonging to the Finnish Disease Heritage were overrepresented. Forty‐eight cases remained undefined. The diagnostic yield (ie, pathogenic or likely pathogenic variants) of next‐generation sequencing (NGS) in ataxia was 65%. Common features were ataxia, developmental delay, seizures, hypotonia, and abnormality in brain MRI, whereas hearing loss, sensory neuropathy, and microcephalia were associated with fewer etiologies.Conclusions: PCDs are a heterogeneous disease group with a high proportion of genetic etiologies. Age of onset and certain clinical findings may help distinguish between different disease entities. The diagnostic yield of NGS has increased over time. Our dataset will support clinicians to recognize PCDs, their co‐morbidities, and genetic etiologies. Further data on epidemiology, shared disease mechanisms, and the natural history of PCDs will be critical for the development of treatment approaches. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Aim: To describe the specific brain magnetic resonance imaging (MRI) patterns of the paediatric genetic disorders associated with white matter abnormalities in Northern Finland.Method: In this retrospective population‐based longitudinal study, brain MRI scans accumulated from 1990 to 2019 at Oulu University Hospital, Finland, were assessed. Inclusion criteria were defined as leukodystrophies or genetic diseases with significant white matter abnormalities that did not meet the criteria for leukodystrophy, at least one brain MRI, and age under 18 years at diagnosis.Results: A total of 83 patients (48 males, 35 females) were found with 52 different diseases. The median age at the time of the brain MRI was 22 months (interquartile range [IQR] = 46 months). In 72 (87%) of the children, brain MRIs revealed abnormal findings, including cerebral white matter abnormalities (n = 49, 59%), brainstem signal abnormalities (n = 28, 34%), thinning of the corpus callosum (n = 30, 36%), delayed myelination (n = 11, 13%), and permanent hypomyelination (n = 9, 11%).Interpretation: Symmetrical and bilateral white matter signal patterns of the brain MRI should raise suspicion of genetic disorders when the clinical symptoms are compatible. This study illustrates brain imaging patterns of childhood‐onset genetic disorders in a population in Northern Finland and improves the diagnostic accuracy of rare genetic disorders.