Human Cortex Research Articles

7036 Luteinizing Hormone/Human Chorionic Gonadotropin Receptor And Cyclooxygenase-2 Expression In Adrenocortical Carcinoma

Abstract Disclosure: R. Sheskier: None. A. Kirschenbaum: None. I. Zappi: None. S. Yao: None. A.C. Levine: None. Adrenocortical carcinoma (ACC) is a rare malignancy that has a poor prognosis. The current mainstays of therapy are surgical resection of primary and oligometastases, and systemic therapy with mitotane with or without combination of etoposide, doxorubicin, and cisplatin for more advanced disease. New therapeutic targets are needed to increase options for medical management of mitotane-resistant disease. Expression and function of the luteinizing hormone/human chorionic gonadotropin receptor (LH/hCG-R) have been demonstrated in non-gonadal tissues and malignancies (1). In the adrenal gland, LH/hCG-R expression has been noted in some secretory benign adenomas but its expression in ACC is underexplored (1, 2). Cyclooxygenase-2 (COX-2) is expressed in a variety of cancers, but has not been previously evaluated in ACC (3). We examined the immunohistochemical expression of LH/hCG-R and COX-2 in 15 tissue samples from 7 patients with ACC of varying ENSAT stages with 5 samples from primary ACC tumors and 10 metastatic samples from varying sites. LH/hCG-R was expressed in 13/15 ACC tissue samples (6/7 patients; 4/5 primary ACC; 9/10 metastases). COX-2 expression was demonstrated in 12/15 ACC tissue samples (6/7 patients; 4/5 primary ACC; 8/10 metastases with one metastasis post-mitotane negative for COX-2). Specimens from one patient were also examined for LH/hCG-R and COX-2 expression prior to and following treatment with mitotane. Compared to the primary ACC tumor pre-treatment with mitotane, the post-mitotane ACC metastasis showed an increase in LH/hCG-R expression and a decrease in COX-2 expression. In comparison, a liver ACC metastasis from a separate patient with mitotane-resistant disease demonstrated persistent LH/hCG-R and COX-2 expression. Our findings suggest that LH/hCG-R and COX-2 are expressed in the majority of ACC primary and metastatic lesions. Our results further imply that LH/hCG-R expression persists in mitotane-resistant disease and COX-2 expression initially decreases with mitotane therapy, but is re-expressed in mitotane-resistant disease. Both LH/hCG-R and COX-2 may therefore serve as potential therapeutic targets in the management of advanced ACC. (1) Carlson HE. Human adrenal cortex hyperfunction due to LH/hCG. Mol Cell Endocrinol. 2007 Apr 15;269(1-2):46-50. (2) Doroszko DM, Chrusciel M, Stelmaszewska J, Slezak T, Anisimowicz S, Plöckinger U, Quinkler M, Bonomi M, Wolczynski S, Huhtaniemi I, Toppari J, Rahman NA. GnRH antagonist treatment of malignant adrenocortical tumors. Endocr Relat Cancer. 2019 Jan 1;26(1):103-117. (3) Hu Z, Yang Y, Zhao Y, Huang Y. The prognostic value of cyclooxygenase-2 expression in patients with esophageal cancer: evidence from a meta-analysis. Onco Targets Ther. 2017 Jun 9;10:2893-2901. Presentation: 6/1/2024

Multi-scale spiking network model of human cerebral cortex.

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

Serotonin-2B receptor (5-HT2BR) expression and binding in the brain of APPswe/PS1dE9 transgenic mice and in Alzheimer’s disease brain tissue

Despite well-documented dysregulation in central serotonergic signaling in Alzheimer’s disease (AD), knowledge about the potential involvement of the serotonin-2B receptor (5-HT2BR) subtype remains sparse. Here, we assessed the levels of 5-HT2BRs in brain tissue from APPswe/PS1dE9 transgenic (TG) mice, AD patients, and adult microglial cells. 5-HT2BR mRNA was measured by RT-qPCR in ageing TG and wild-type (WT) mice, in samples from the middle frontal gyrus of female, AD and control subjects, and in microglia from the cerebral cortex of WT mice. The density of 5-HT2BRs was measured by autoradiography using [3H]RS 127445. Both mouse and human brains had low levels of 5-HT2BR mRNA. In whole-brain mouse samples, mRNA expression was significantly lower in TG mice compared to WT at > 18 months of age. In the Aβ-plaque-burdened neocortex and hippocampus of old TG mice, however, levels of 5-HT2BR mRNA were two-fold higher over control, with similar elevations observed in the Aβ-plaque-burdened frontal cortex of human AD patients. 5-HT2BR mRNA expression varied widely in adult microglia and was higher compared to other cortical cell subtypes. In mice, specific [3H]RS-127445 binding in the cortex was first detected after 3 months of age. The density of 5-HT2BRs was low and overall reduced in TG, compared to WT mice. Binding was detectable but too low to be reliably quantified in the human cortex. Our results document Aβ-associated increases in 5-HT2BR mRNA expression and suggest reduced receptor binding in the context of AD. Studies investigating the functional involvement of microglial 5-HT2BRs in AD are considered relevant.

Neuronal current imaging of epileptic activity: An MRI study in patients with a first unprovoked epileptic seizure.

This study evaluates the performance of the novel MRI sequence stimulus-induced rotary saturation (SIRS) to map responses to interictal epileptic activity in the human cortex. Spin-lock pulses have been applied to indirectly detect neuronal activity through magnetic field perturbations. Following initial reports about the feasibility of the method in humans and animals with epilepsy, we aimed to investigate the diagnostic yield of spin-lock MR pulses in comparison with scalp-EEG in first seizure patients. We employed a novel method for measurements of neuronal activity through the detection of a resonant oscillating field, stimulus-induced rotary saturation contrast (SIRS) at spin-lock frequencies of 120 and 240 Hz acquired at a single 3T MRI system. Within a prospective observational study, we conducted SIRS experiments in 55 patients within 7 days after a suspected first unprovoked epileptic seizure and 61 healthy control subjects. In this study, we report on the analysis of data from a single 3T MRI system, encompassing 35 first seizure patients and 31 controls. The SIRS method was applicable in all patients and healthy controls at frequencies of 120 and 240 Hz. We did not observe any significant age- or sex-related differences. Specificity of SIRS at 120 Hz was 90.3% and 93.5% at 240 Hz. Sensitivity was 17.1% at 120 Hz and 40.0% at 240 Hz. SIRS targets neuronal oscillating magnetic fields in patients with epilepsy. The coupling of presaturated spins to epilepsy-related magnetic field perturbations may serve as a-at this stage experimental-diagnostic test in first seizure patients to complement EEG findings as a standard screening test. Routine diagnostic tests carry several limitations when applied after a suspected first seizure. SIRS is a noninvasive MRI method to enable time-sensitive diagnosis of image correlates of epileptic activity with increased sensitivity compared to routine EEG.

ID: 330948 Invasive and Non-invasive Synergies Between Motor Cortex and Cervical Spinal Stimulation in Humans

ID: 330948 Invasive and Non-invasive Synergies Between Motor Cortex and Cervical Spinal Stimulation in Humans

Attentional processing in the rat dorsal posterior parietal cortex

The human posterior parietal cortex (PPC) is known to support sustained attention. Specifically, top-down attention is generally processed in dorsal regions while bottom-up regulation occurs more ventrally. In rodent models, however, it is still unclear whether the PPC is required for sustained attention, or whether there is a similar functional dissociation between anatomical regions. Consequently, the aim of this study was to investigate the contribution of the rodent dorsal PPC (dPPC) in sustained attention. We used the five-choice serial reaction time task (5CSRTT) and compared rats with neurotoxic dPPC lesions to sham operated rats. We found that rats with dPPC lesions were less accurate and took longer to make correct choices, indicating impaired attention and reduced processing speed. This effect, however, was limited to the first few days of post-operative testing. After an apparent recovery, omissions became elevated in the lesion group, which, in the absence of reduced motivation and mobility, can also be interpreted as impaired attention. In subsequent challenge probes, the lesion group displayed globally elevated latency to make a correct response, indicating reduced processing speed. No differences in premature responses or perseverative responses were observed at any time, demonstrating that dPPC lesions did not affect impulsivity and compulsivity. This pattern of behavior suggests that while intact dPPC supports goal-driven (top-down) modulation of attention, it likely does not play a central role in processing stimulus-driven (bottom-up) attention. Furthermore, compensatory mechanisms can support sustained attention in the absence of a fully functioning dPPC, although this occurs at the expense of processing speed. Our results inform the literature by confirming that rodent PPC is involved in regulating sustained attention and providing preliminary evidence for a functional dissociation between top-down and bottom-up attentional processing.

Neuronal lineage tracing from progenitors in human cortical organoids reveals mechanisms of neuronal production, diversity, and disease

The contribution of progenitor subtypes to generating the billions of neurons produced during human cortical neurogenesis is not well understood. We developed the cortical organoid lineage-tracing (COR-LT) system for human cortical organoids. Differential fluorescent reporter activation in distinct progenitor cells leads to permanent reporter expression, enabling the progenitor cell lineage of neurons to be determined. Surprisingly, nearly all excitatory neurons produced in cortical organoids were generated indirectly from intermediate progenitor cells. Additionally, neurons of different progenitor lineages were transcriptionally distinct. Isogenic lines made from an autistic individual with and without a likely pathogenic CTNNB1 variant demonstrated that the variant substantially altered the proportion of neurons derived from specific progenitor cell lineages, as well as the lineage-specific transcriptional profiles of these neurons, suggesting a pathogenic mechanism for this mutation. These results suggest individual progenitor subtypes play roles in generating the diverse neurons of the human cerebral cortex.

Anatomical and molecular development of the human primary visual cortex.

The human primary visual cortex (V1) development is pivotal to understanding cortical maturation and neuroplasticity. Theories on V1 development range from early maturation models, which emphasize the early peak of synapses in infancy, to those suggesting an extended developmental timeline where key plasticity mechanisms continue to mature well into adulthood. Classic histological approaches have supported early development, while recent molecular studies highlight prolonged or multiple windows of plasticity, indicating that V1 remains susceptible to experience-dependent modifications beyond childhood. This review consolidates findings from both anatomical and molecular studies, tracing the development of V1 from prenatal stages through aging. The evidence reveals that human V1 develops across multiple timescales, with some aspects maturing early and others gradually changing across the lifespan. Reflecting on Cajal's early work, this review underscores the importance of methodological advancements in revealing the intricate details of V1's development.

Identifying Brain Network Structure for an fMRI Effective Connectivity Study Using the Least Absolute Shrinkage and Selection Operator (LASSO) Method.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

FMRI speech tracking in primary and non-primary auditory cortex while listening to noisy scenes

Invasive and non-invasive electrophysiological measurements during “cocktail-party”-like listening indicate that neural activity in the human auditory cortex (AC) “tracks” the envelope of relevant speech. However, due to limited coverage and/or spatial resolution, the distinct contribution of primary and non-primary areas remains unclear. Here, using 7-Tesla fMRI, we measured brain responses of participants attending to one speaker, in the presence and absence of another speaker. Through voxel-wise modeling, we observed envelope tracking in bilateral Heschl’s gyrus (HG), right middle superior temporal sulcus (mSTS) and left temporo-parietal junction (TPJ), despite the signal’s sluggish nature and slow temporal sampling. Neurovascular activity correlated positively (HG) or negatively (mSTS, TPJ) with the envelope. Further analyses comparing the similarity between spatial response patterns in the single speaker and concurrent speakers conditions and envelope decoding indicated that tracking in HG reflected both relevant and (to a lesser extent) non-relevant speech, while mSTS represented the relevant speech signal. Additionally, in mSTS, the similarity strength correlated with the comprehension of relevant speech. These results indicate that the fMRI signal tracks cortical responses and attention effects related to continuous speech and support the notion that primary and non-primary AC process ongoing speech in a push-pull of acoustic and linguistic information.

Contrastive learning explains the emergence and function of visual category-selective regions.

Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework-contrastive coding-based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.

Comparative transcriptomic rhythms in the mouse and human prefrontal cortex.

Alterations in multiple subregions of the human prefrontal cortex (PFC) have been heavily implicated in psychiatric diseases. Moreover, emerging evidence suggests that circadian rhythms in gene expression are present across the brain, including in the PFC, and that these rhythms are altered in disease. However, investigation into the potential circadian mechanisms underlying these diseases in animal models must contend with the fact that the human PFC is highly evolved and specialized relative to that of rodents. Here, we use RNA sequencing to lay the groundwork for translational studies of molecular rhythms through a sex-specific, cross species comparison of transcriptomic rhythms between the mouse medial PFC (mPFC) and two subregions of the human PFC, the anterior cingulate cortex (ACC) and the dorsolateral PFC (DLPFC). We find that while circadian rhythm signaling is conserved across species and subregions, there is a phase shift in the expression of core clock genes between the mouse mPFC and human PFC subregions that differs by sex. Furthermore, we find that the identity of rhythmic transcripts is largely unique between the mouse mPFC and human PFC subregions, with the most overlap (20%, 236 transcripts) between the mouse mPFC and the human ACC in females. Nevertheless, we find that basic biological processes are enriched for rhythmic transcripts across species, with key differences between regions and sexes. Together, this work highlights both the evolutionary conservation of transcriptomic rhythms and the advancement of the human PFC, underscoring the importance of considering cross-species differences when using animal models.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources.

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

Temporal integration in human auditory cortex is predominantly yoked to absolute time, not structure duration.

Sound structures such as phonemes and words have highly variable durations. Thus, there is a fundamental difference between integrating across absolute time (e.g., 100 ms) vs. sound structure (e.g., phonemes). Auditory and cognitive models have traditionally cast neural integration in terms of time and structure, respectively, but the extent to which cortical computations reflect time or structure remains unknown. To answer this question, we rescaled the duration of all speech structures using time stretching/compression and measured integration windows in the human auditory cortex using a new experimental/computational method applied to spatiotemporally precise intracranial recordings. We observed significantly longer integration windows for stretched speech, but this lengthening was very small (~5%) relative to the change in structure durations, even in non-primary regions strongly implicated in speech-specific processing. These findings demonstrate that time-yoked computations dominate throughout the human auditory cortex, placing important constraints on neurocomputational models of structure processing.

Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks.

The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure-function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.

Lasting effects of transcranial direct current stimulation on the inducibility of synaptic plasticity by paired-associative stimulation in humans

BackgroundTranscranial direct current stimulation (tDCS) is capable of eliciting changes in cortical neuroplasticity. Increasing duration or repetition of tDCS during the after-effects of a first stimulation has been hypothesized to enhance efficacy. Computational models suggest sequential stimulation patterns with changing polarities to further enhance effects. Lasting tDCS effects on neural plasticity are of great importance for clinical applications.ObjectiveThe study systematically examined the influence of different tDCS paradigms on long term potentiation (LTP)-like plasticity in humans, focusing on stimulation duration, repetition frequency and sequential combinations of changing polarities as the underlying characteristics.MethodsAmplitude changes of motor evoked potentials (MEP) were measured in response to paired associative stimulation (PAS) 6 h after application of different tDCS protocols. In total, 36 healthy participants completed the study, randomised into three groups with different stimulation protocols (N = 12 each).ResultstDCS was able to display lasting modulatory effects on the inducibility of LTP-like plasticity in the human motor cortex 6 h after stimulation. TDCS with the anode on primary motor cortex significantly increased MEP amplitudes following PAS induction. Further analyses highlighted single stimulation block duration to be of higher importance than repetitive protocols for efficacy of effects.ConclusionstDCS is capable of inducing lasting changes in the brain’s capability to interact with future stimuli. Especially, effects on the inducibility of LTP-like plasticity might only be detectable with specific tests such as PAS and might otherwise be overlooked. Refined tDCS protocols should focus on higher current and duration of single stimulations instead of implementing complex repetitive schedules.

Automated data-driven discovery of material models based on symbolic regression: A case study on the human brain cortex

We introduce a data-driven framework to automatically identify interpretable and physically meaningful hyperelastic constitutive models from sparse data. Leveraging symbolic regression, our approach generates elegant hyperelastic models that achieve accurate data fitting with parsimonious mathematic formulas, while strictly adhering to hyperelasticity constraints such as polyconvexity/ellipticity. Our investigation spans three distinct hyperelastic models—invariant-based, principal stretch-based, and normal strain-based—and highlights the versatility of symbolic regression. We validate our new approach using synthetic data from five classic hyperelastic models and experimental data from the human brain cortex to demonstrate algorithmic efficacy. Our results suggest that our symbolic regression algorithms robustly discover accurate models with succinct mathematic expressions in invariant-based, stretch-based, and strain-based scenarios. Strikingly, the strain-based model exhibits superior accuracy, while both stretch-based and strain-based models effectively capture the nonlinearity and tension-compression asymmetry inherent to the human brain tissue. Polyconvexity/ellipticity assessment affirm the rigorous adherence to convexity requirements both within and beyond the training regime. However, the stretch-based models raise concerns regarding potential convexity loss under large deformations. The evaluation of predictive capabilities demonstrates remarkable interpolation capabilities for all three models and acceptable extrapolation performance for stretch-based and strain-based models. Finally, robustness tests on noise-embedded data underscore the reliability of our symbolic regression algorithms. Our study confirms the applicability and accuracy of symbolic regression in the automated discovery of isotropic hyperelastic models for the human brain and gives rise to a wide variety of applications in other soft matter systems. Statement of significanceOur research introduces a pioneering data-driven framework that revolutionizes the automated identification of hyperelastic constitutive models, particularly in the context of soft matter systems such as the human brain. By harnessing the power of symbolic regression, we have unlocked the ability to distill intricate physical phenomena into elegant and interpretable mathematical expressions. Our approach not only ensures accurate fitting to sparse data but also upholds crucial hyperelasticity constraints, including polyconvexity, essential for maintaining physical relevance.

A novel DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity

BackgroundDue to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results. New methodHere, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding. ResultsExperimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction. Comparison with existing methodsWe qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance. ConclusionsDRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.

Manipulating attentional priority creates a trade-off between memory and sensory representations in human visual cortex.

People often remember visual information over brief delays while actively engaging with ongoing inputs from the surrounding visual environment. Depending on the situation, one might prioritize mnemonic contents (i.e., remembering details of a past event), or preferentially attend sensory inputs (i.e., minding traffic while crossing a street). Previous fMRI work has shown that early sensory regions can simultaneously represent both mnemonic and passively viewed sensory information. Here we test the limits of such simultaneity by manipulating attention towards sensory distractors during a working memory task performed by human subjects during fMRI scanning. Participants remembered the orientation of a target grating while a distractor grating was shown during the middle portion of the memory delay. Critically, there were several subtle changes in the contrast and the orientation of the distractor, and participants were cued to either ignore the distractor, detect a change in contrast, or detect a change in orientation. Despite sensory stimulation being matched in all three conditions, the fidelity of memory representations in early visual cortex was highest when the distractor was ignored, intermediate when participants attended distractor contrast, and lowest when participants attended the orientation of the distractor during the delay. In contrast, the fidelity of distractor representations was lowest when ignoring the distractor, intermediate when attending distractor-contrast, and highest when attending distractor-orientation. These data suggest a trade-off in early sensory representations when engaging top-down feedback to attend both seen and remembered features and may partially explain memory failures that occur when subjects are distracted by external events.

Cortical sites critical to language function act as connectors between language subnetworks

Historically, eloquent functions have been viewed as localized to focal areas of human cerebral cortex, while more recent studies suggest they are encoded by distributed networks. We examined the network properties of cortical sites defined by stimulation to be critical for speech and language, using electrocorticography from sixteen participants during word-reading. We discovered distinct network signatures for sites where stimulation caused speech arrest and language errors. Both demonstrated lower local and global connectivity, whereas sites causing language errors exhibited higher inter-community connectivity, identifying them as connectors between modules in the language network. We used machine learning to classify these site types with reasonably high accuracy, even across participants, suggesting that a site’s pattern of connections within the task-activated language network helps determine its importance to function. These findings help to bridge the gap in our understanding of how focal cortical stimulation interacts with complex brain networks to elicit language deficits.