Thalamostriatal disconnection underpins long-term seizure freedom in frontal lobe epilepsy surgery.

Around 50% of patients undergoing frontal lobe surgery for focal drug-resistant epilepsy become seizure free post-operatively; however, only about 30% of patients remain seizure free in the long-term. Early seizure recurrence is likely to be caused by partial resection of the epileptogenic lesion, whilst delayed seizure recurrence can occur even if the epileptogenic lesion has been completely excised. This suggests a coexistent epileptogenic network facilitating ictogenesis in close or distant dormant epileptic foci. As thalamic and striatal dysregulation can support epileptogenesis and disconnection of cortico-thalamostriatal pathways through hemispherotomy or neuromodulation can improve seizure outcome regardless of focality, we hypothesize that projections from the striatum and the thalamus to the cortex may contribute to this common epileptogenic network. To this end, we retrospectively reviewed a series of 47 consecutive individuals who underwent surgery for drug-resistant frontal lobe epilepsy. We performed voxel-based and tractography disconnectome analyses to investigate shared patterns of disconnection associated with long-term seizure freedom. Seizure freedom after 3 and 5 years was independently associated with disconnection of the anterior thalamic radiation and anterior cortico-striatal projections. This was also confirmed in a subgroup of 29 patients with complete resections, suggesting these pathways may play a critical role in supporting the development of novel epileptic networks. Our study indicates that network dysfunction in frontal lobe epilepsy may extend beyond the resection and putative epileptogenic zone. This may be critical in the pathogenesis of delayed seizure recurrence as thalamic and striatal networks may promote epileptogenesis and disconnection may underpin long-term seizure freedom.

Two critical brain networks for generation and combination of remote associations.

Recent functional imaging findings in humans indicate that creativity relies on spontaneous and controlled processes, possibly supported by the default mode and the fronto-parietal control networks, respectively. Here, we examined the ability to generate and combine remote semantic associations, in relation to creative abilities, in patients with focal frontal lesions. Voxel-based lesion-deficit mapping, disconnection-deficit mapping and network-based lesion-deficit approaches revealed critical prefrontal nodes and connections for distinct mechanisms related to creative cognition. Damage to the right medial prefrontal region, or its potential disrupting effect on the default mode network, affected the ability to generate remote ideas, likely by altering the organization of semantic associations. Damage to the left rostrolateral prefrontal region and its connections, or its potential disrupting effect on the left fronto-parietal control network, spared the ability to generate remote ideas but impaired the ability to appropriately combine remote ideas. Hence, the current findings suggest that damage to specific nodes within the default mode and fronto-parietal control networks led to a critical loss of verbal creative abilities by altering distinct cognitive mechanisms.

Reasoning by analogy requires the left frontal pole: lesion-deficit mapping and clinical implications.

SEE BURGESS DOI101093/BRAIN/AWW092 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE : Analogical reasoning is at the core of the generalization and abstraction processes that enable concept formation and creativity. The impact of neurological diseases on analogical reasoning is poorly known, despite its importance in everyday life and in society. Neuroimaging studies of healthy subjects and the few studies that have been performed on patients have highlighted the importance of the prefrontal cortex in analogical reasoning. However, the critical cerebral bases for analogical reasoning deficits remain elusive. In the current study, we examined analogical reasoning abilities in 27 patients with focal damage in the frontal lobes and performed voxel-based lesion-behaviour mapping and tractography analyses to investigate the structures critical for analogical reasoning. The findings revealed that damage to the left rostrolateral prefrontal region (or some of its long-range connections) specifically impaired the ability to reason by analogies. A short version of the analogy task predicted the existence of a left rostrolateral prefrontal lesion with good accuracy. Experimental manipulations of the analogy tasks suggested that this region plays a role in relational matching or integration. The current lesion approach demonstrated that the left rostrolateral prefrontal region is a critical node in the analogy network. Our results also suggested that analogy tasks should be translated to clinical practice to refine the neuropsychological assessment of patients with frontal lobe lesions.

Deep learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms.

This study investigates the efficacy of deep-learning models in expediting the generation of disconnectomes for individualized prediction of neuropsychological outcomes one year after stroke. Utilising a 3D U-Net network, we trained a model on a dataset of N = 1333 synthetic lesions and corresponding disconnectomes, subsequently applying it to N = 1333 real stroke lesions. The model-generated disconnection patterns were then projected into a two-dimensional 'morphospace' via uniform manifold approximation and projection for dimension reduction dimensionality reduction. We correlated the positioning within this morphospace with one-year neuropsychological scores across 86 metrics in a novel cohort of 119 stroke patients, employing multiple regression models and validating the findings in an out-of-sample group of 20 patients. Our results demonstrate that the 3D U-Net model captures the critical information of conventional disconnectomes with a notable increase in efficiency, generating deep-disconnectomes 720 times faster than current state-of-the-art software. The predictive accuracy of neuropsychological outcomes by deep-disconnectomes averaged 85.2% (R 2 = 0.208), which significantly surpassed the conventional disconnectome approach (P = 0.009). These findings mark a substantial advancement in the production of disconnectome maps via deep learning, suggesting that this method could greatly enhance the prognostic assessment and clinical management of stroke survivors by incorporating disconnection patterns as a predictive tool.

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023.

Stroke is a significant cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. The NeuralCup 2023 consortium was established to address these challenges by comparing the predictability of stroke outcome models through a collaborative, data-driven approach. This study presents the consortium's findings, which involved 15 participating teams worldwide. Using a comprehensive dataset, which included clinical and imaging data, we conducted an open competition to identify and compare predictors of motor, cognitive, and neuropsychological (emotional) outcomes one-year post-stroke. Analyses incorporated both traditional and novel methods, including machine learning algorithms. These efforts culminated in the search for 'optimal recipes' for predicting each domain through an exhaustive exploration of the features of all the approaches. Key predictors included lesion characteristics, T1-weighted MRI sequences, and demographic factors. Notably, integrating FLAIR imaging and white matter tract analysis emerged as crucial to improving the accuracy of cognitive and motor outcome predictions, respectively. These findings advocate for a tailored, multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science in addressing complex neurological prognostication challenges. This study also sets a new benchmark methodology in stroke research, offering a foundational step toward personalized care strategies that could significantly impact recovery planning and quality of life for stroke survivors.

The human cost of ethical artificial intelligence.

Foundational models such as ChatGPT critically depend on vast data scales the internet uniquely enables. This implies exposure to material varying widely in logical sense, factual fidelity, moral value, and even legal status. Whereas data scaling is a technical challenge, soluble with greater computational resource, complex semantic filtering cannot be performed reliably without human intervention: the self-supervision that makes foundational models possible at least in part presupposes the abilities they seek to acquire. This unavoidably introduces the need for large-scale human supervision-not just of training input but also model output-and imbues any model with subjectivity reflecting the beliefs of its creator. The pressure to minimize the cost of the former is in direct conflict with the pressure to maximise the quality of the latter. Moreover, it is unclear how complex semantics, especially in the realm of the moral, could ever be reduced to an objective function any machine could plausibly maximise. We suggest the development of foundational models necessitates urgent innovation in quantitative ethics and outline possible avenues for its realisation.

Functionnectome as a framework to analyse the contribution of brain circuits to fMRI.

In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain's functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits' anatomy to unlock and chart the first maps of functional white matter. To showcase this method's versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.

Brain disconnections link structural connectivity with function and behaviour.

Brain lesions do not just disable but also disconnect brain areas, which once deprived of their input or output, can no longer subserve behaviour and cognition. The role of white matter connections has remained an open question for the past 250 years. Based on 1333 stroke lesions, here we reveal the human Disconnectome and demonstrate its relationship to the functional segregation of the human brain. Results indicate that functional territories are not only defined by white matter connections, but also by the highly stereotyped spatial distribution of brain disconnections. While the former has granted us the possibility to map 590 functions on the white matter of the whole brain, the latter compels a revision of the taxonomy of brain functions. Overall, our freely available Atlas of White Matter Function will enable improved clinical-neuroanatomical predictions for brain lesion studies and provide a platform for explorations in the domain of cognition.

An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings.

The brain is constituted of multiple networks of functionally correlated brain areas, out of which the default-mode network (DMN) is the largest. Most existing research into the DMN has taken a corticocentric approach. Despite its resemblance with the unitary model of the limbic system, the contribution of subcortical structures to the DMN may be underappreciated. Here, we propose a more comprehensive neuroanatomical model of the DMN including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and mediodorsal thalamic nuclei. Additionally, tractography of diffusion-weighted imaging was employed to explore the structural connectivity, which revealed that the thalamus and basal forebrain are of central importance for the functioning of the DMN. The contribution of these neurochemically diverse brain nuclei reconciles previous neuroimaging with neuropathological findings in diseased brains and offers the potential for identifying a conserved homologue of the DMN in other mammalian species.

Anosognosia for hemiplegia as a tripartite disconnection syndrome.

The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit.

Background: Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions. Findings: Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. Conclusions: The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].