Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS: SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS: Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS: These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia, progressively impairing memory and cognition. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify abnormal biophysical mechanisms underlying these abnormal electrophysiological patterns, we estimated the parameters of a spectral graph-theory model (SGM). SGM is an analytic model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. The long-range excitatory time scale was associated with greater deficits in global cognition and was able to distinguish AD patients from controls with high accuracy. These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the spatiospectral signatures and cognition in AD.

Mechanically active integrins target lytic secretion at the immune synapse to facilitate cellular cytotoxicity.

Cytotoxic lymphocytes fight pathogens and cancer by forming immune synapses with infected or transformed target cells and then secreting cytotoxic perforin and granzyme into the synaptic space, with potent and specific killing achieved by this focused delivery. The mechanisms that establish the precise location of secretory events, however, remain poorly understood. Here we use single cell biophysical measurements, micropatterning, and functional assays to demonstrate that localized mechanotransduction helps define the position of secretory events within the synapse. Ligand-bound integrins, predominantly the αLß2 isoform LFA-1, function as spatial cues to attract lytic granules containing perforin and granzyme and induce their fusion with the plasma membrane for content release. LFA-1 is subjected to pulling forces within secretory domains, and disruption of these forces via depletion of the adaptor molecule talin abrogates cytotoxicity. We thus conclude that lymphocytes employ an integrin-dependent mechanical checkpoint to enhance their cytotoxic power and fidelity.

Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer's disease.

Background: Neuronal- and circuit-level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aß) in preclinical models of Alzheimer's disease (AD). These relationships remain poorly understood in patients with AD. Methods: Using empirical spectra from magnetoencephalography and computational modeling (neural mass model), we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aß, measured by positron emission tomography, in patients with AD. Results: Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aß depositions. Conclusions: Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aß in patients with AD. Funding: This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM and GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer's Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL (KGR), 2019-A-013-SUP (KGR); grants from the Alzheimer's Association: AARG-21-849773 (KGR); PCTRB-13-288476 (KAV), and made possible by Part the CloudTM (ETAC-09-133596); a grant from Tau Consortium (GDR and WJJ), and a gift from the S. D. Bechtel Jr. Foundation.

Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging.

Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.

Emergence of canonical functional networks from the structural connectome.

How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its "structural connectome". By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Spectral graph theory of brain oscillations.

The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structure-function problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled nonlinear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30 Hz) activity estimated from source localized magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. .

Gemcitabine treatment enhanced the anti-tumor effect of cytokine induced killer cells by depletion of CD4+CD25bri regulatory T cells.

Cytokine induced killer (CIK) cells have a powerful tumor cells killing activity both in vitro and in vivo and transfusion of these cells have become an adjuvant treatment for tumors. CIK cells are induced and amplified from peripheral blood mononuclear cells (PBMCs) with multiple cytokines. As CD4+CD25bri regulatory T cells can be also induced by high dose of interleukin 2 (IL-2) which is used for CIK cells amplification in the CIK cell culture system, the anti-tumor activity of CIK cells was suppressed to some extent. In order to overcome this unwanted suppressive factor, we found that low dose of gemcitabine could reduce the proportion of CD4+CD25bri regulatory T cells in the CIK cell culture system and significantly enhance the anti-tumor activity of CIK cells in vitro. The levels of interleukin-10 (IL-10) and transforming growth factor-ß (TGF-ß) were also reduced significantly following the depletion of CD4+CD25bri regulatory T cells in gemcitabine treated CIK cell culture system. In vivo experiment showed that low dose of gemcitabine treated CIK cells significantly suppressed tumor growth and prolonged their lifespan in tumor-bearing nude mice, with the proportion of CD4+CD25bri regulatory T cells reduced. Meanwhile, we detected lower levels of IL-10, TGF-ß and a higher level of interferon-γ (IFN-γ) in tumor-bearing nude mice that received gemcitabine treated CIK cells transfusion than those in other groups. The possible mechanism involved in the enhanced anti-tumor activity in vivo was that gemcitabine treated CIK cells created a strengthened anti-tumor immune microenvironment with the changed levels of cytokines such as IL-10, TGF-ß and IFN-γ. These results suggested a strategy to improve the adoptive immune therapy in recent use by removing the suppressive factors and a more effective tumor treatment combining chemotherapy and immunotherapy.

Dendritic cell immunotherapy combined with cytokine-induced killer cells promotes skewing toward Th2 cytokine profile in patients with metastatic non-small cell lung cancer.

Dendritic cell (DC) vaccination and cytokine-induced killer (CIK) cell therapy (DC/CIK) have shown limited success in the treatment of advanced non-small cell lung cancer (NSCLC). To investigate the reason for this limited success, the effects of DC/CIK cell therapy on the immune responses of tumor-bearing patients and patients with resected NSCLC were evaluated. In the total 50 patients studied, the serum concentrations of the Th2 cytokines (IL-4 and IL-10) in tumor-bearing patients were significantly higher than those with resected NSCLC before immunotherapy. The post-therapy Th1 cytokine (IFN-γ) level in patients with resected NSCLC significantly increased from the pre-therapy level. In contrast, significantly enhanced post-therapy Th2 cytokine (IL-4 and IL-10) levels were found in tumor-bearing patients. The intracellular staining assay revealed that DC/CIK cell therapy increased the IFN-γ-producing T lymphocyte (CD8(+)IFN-γ(+)) frequency in patients with resected NSCLC, but these lymphocytes were not found in tumor-bearing patients. Furthermore, overproduction of vascular endothelial growth factor (VEGF) in tumor-bearing patients showed a statistically positive correlation with IL-4, suggesting that VEGF might be responsible for the predominance of serum Th2 cytokines. In a word, tumor-bearing patients developed a Th2-dominant status that could not be reversed toward Th1 following immunotherapy. A combined regiment of DC vaccination and CIK cell therapy with other treatments to overcome systemic Th2-dominant immune response might improve the current clinical benefit.

Autologous tumor lysate-pulsed dendritic cell immunotherapy with cytokine-induced killer cells improves survival in gastric and colorectal cancer patients.

Gastric and colorectal cancers (GC and CRC) have poor prognosis and are resistant to chemo- and/or radiotherapy. In the present study, the prophylactic effects of dendritic cell (DC) vaccination are evaluated on disease progression and clinical benefits in a group of 54 GC and CRC patients treated with DC immunotherapy combined with cytokine-induced killer (CIK) cells after surgery with or without chemo-radiotherapy. DCs were prepared from the mononuclear cells isolated from patients using IL-2/GM-CSF and loaded with tumor antigens; CIK cells were prepared by incubating peripheral blood lymphocytes with IL-2, IFN-γ, and CD3 antibodies. The DC/CIK therapy started 3 days after low-dose chemotherapy and was repeated 3-5 times in 2 weeks as one cycle with a total of 188.3 ± 79.8 × 10(6) DCs and 58.8 ± 22.3 × 10(8) CIK cells. Cytokine levels in patients' sera before and after treatments were measured and the follow-up was conducted for 98 months to determine disease-free survival (DFS) and overall survival (OS). The results demonstrate that all cytokines tested were elevated with significantly higher levels of IFN-γ and IL-12 in both GC and CRC cohorts of DC/CIK treated patients. By Cox regression analysis, DC/CIK therapy reduced the risk of post-operative disease progression (p<0.01) with an increased OS (<0.01). These results demonstrate that in addition to chemo- and/or radiotherapy, DC/CIK immunotherapy is a potential effective approach in the control of tumor growth for post-operative GC and CRC patients.

Clinician accessible tools for GUI computational models of transcranial electrical stimulation: BONSAI and SPHERES.

Computational models of brain current flow during transcranial electrical stimulation (tES), including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), are increasingly used to understand and optimize clinical trials. We propose that broad dissemination requires a simple graphical user interface (GUI) software that allows users to explore and design montages in real-time, based on their own clinical/experimental experience and objectives. We introduce two complimentary open-source platforms for this purpose: BONSAI and SPHERES. BONSAI is a web (cloud) based application (available at neuralengr.com/bonsai) that can be accessed through any flash-supported browser interface. SPHERES (available at neuralengr.com/spheres) is a stand-alone GUI application that allow consideration of arbitrary montages on a concentric sphere model by leveraging an analytical solution. These open-source tES modeling platforms are designed go be upgraded and enhanced. Trade-offs between open-access approaches that balance ease of access, speed, and flexibility are discussed.

Post-transcriptional CD59 gene silencing by siRNAs induces enhanced human T lymphocyte response to tumor cell lysate-loaded DCs.

CD59 is a complement regulatory protein known to prevent the membrane attack complex (MAC) from assembling. To investigate the role of CD59 molecules in human T cell activation in response to exogenous antigens, gene silencing via small interfering RNAs (siRNAs) was carried out. Subsequent T cell activation in response to both autologous dendritic cells (DCs) loaded with tumor lysate and beads coated with anti-CD3, anti-CD28 and anti-CD59 antibodies was investigated. The findings demonstrated that decreased CD59 expression on T cells significantly enhanced activation and proliferation of CD4(+) T cells and CD8(+) T cells while the expansion of CD4(+) CD25(+) regulatory T cells (Tregs) was not affected, and CD59 mediated inhibition of T cell activation requires the binding of CD59 with its ligand on antigen-presenting cells (APCs). The data support that CD59 down-regulates antigen-specific activation of human T lymphocytes in a ligand-dependent manner.