Illuminating Neural Circuits: From Molecules to MRI

Abstract

Neurological disease drives symptoms through pathological changes to circuit functions. Therefore, understanding circuit mechanisms that drive behavioral dysfunction is of critical importance for quantitative diagnosis and systematic treatment of neurological disease. Here, we describe key technologies that enable measurement and manipulation of neural activity and neural circuits. Applying these approaches led to the discovery of circuit mechanisms underlying pathological motor behavior, arousal regulation, and protein accumulation. Finally, we discuss how optogenetic functional magnetic resonance imaging reveals global scale circuit mechanisms, and how circuit manipulations could lead to new treatments of neurological diseases.

Keywords: Alzheimer's disease; arousal; basal ganglia; gamma frequency; ofMRI; optogenetics.

Figure 1.

ofMRI with D1-, D2-MSN stimulations. A, The 20 Hz D1-MSN stimulation in dorsomedial striatum results in increase of (B) average firing rate (n = 120, 100%) and (C) number of contralateral rotations (n = 13). ***p < 0.001. D, The 20 Hz D2-MSN stimulation in dorsomedial striatum results in increase of (E) average firing rate (n = 15, 93%) and (F) number of ipsilateral rotations (n = 11). **p < 0.005. G, H, Groupwise activation maps demonstrate the large-scale modulation of cortical and subcortical regions, including the basal ganglia, during D1- and D2-MSN stimulation. Significantly modulated voxels are color-coded according to their phase relative to six repeated cycles of 20 s, 20 Hz stimulations. I, Regions of interest, used for time series extraction in J. J, Average time series of active voxels during D1- and D2-MSN stimulation within each region of interest (left/red traces and right/blue traces, respectively). Values are mean ± SEM across animals (n = 13 D1-MSN; n = 11 D2-MSN). Responses are generally opposite in direction, with the exception of the anterior caudate-putamen and globus pallidus external (GPe). Modified with permission from Lee et al. (2016).

Figure 2.

Computational modeling of ofMRI data reveals parameterized dynamic functional relationship with cell type specificity. A, D1-MSN stimulation ofMRI data with computational modeling show clear direct pathway signaling. B, D2-MSN stimulation ofMRI data with computational modeling show clear indirect pathway and hyperdirect pathway signaling. Modified with permission from Bernal-Casas et al. (2017).

Figure 3.

Evidence for dose-dependent effects of activation of frontostriatal networks by high-frequency stimulation of the central lateral nucleus of the thalamus. A, Fiberoptic optogenetic activation of central lateral thalamic neurons shows frequency dependence (Liu et al., 2015). *p < 0.05, **p < 0.005, ***p < 0.001. B, Spatial characterization of evoked fMRI signals showing average coherence maps of brainwide activity during stimulation of excitatory central thalamus relay neurons at 100 Hz. Warm colors represent positive BOLD responses. Cool colors represent negative BOLD responses. MPFC, Medial prefrontal cortex; LPFC, lateral prefrontal cortex; SC, primary somatosensory cortex; Cg, cingulate cortex; Th, thalamus; MC, motor cortex. C, CT-DBS (Baker et al., 2016). Field shaping CT-DBS via cross-electrode stimulation markedly shifts power spectra of LFPs measured across various regions within the frontal cortex and dorsal striatum (data not shown) in the nonhuman primate. Average LFP power (left of vertical dashed line) and average Z score of LFP power (right side of vertical dashed line (for methods, see Baker et al., 2016) concatenated over a series of 10 trials before fsCT-DBS onset, indicated by dashed vertical line at Trial 0, and 15 trials during fsCT-DBS. LFP activity averaged >1305 separate recording sites within the frontal cortex and aggregated >1325 fsCT-DBS periods conducted in 144 experimental sessions. Stimulation amplitudes ranging from 0.75 to 2.5 mA, and stimulation frequencies of 150, 175, 200, and 225 Hz are included. D, Average Z score of the LFP power spectra shown in C but restricted to the delay period of Correct trials during fsCT-DBS ON periods, relative to the delay period activity of Correct trials recorded during fsCT-DBS OFF periods, including 95% CI. Dose-dependent effect of intensity over three sets of stimulation amplitudes, 0.75–1.25, 1.50–1.75, and 2.0–2.5 mA.

Figure 4.

The path from cellular and molecular pathology to cognitive impairment through circuit dysfunction. Multiple molecular and cellular pathologies, including ApoE4, phosphorylate Tau (pTau), amyloid β, microglia malfunction, and signaling related to inflammation, are thought to contribute to cognitive impairment. How these molecular and cellular pathologies alter circuits to lead to cognitive impairment is not clear and is an active area of investigation, including changes in specific cell types and microcircuits, neural activity during behavior, and large-scale networks.

Figure 5.

Driving gamma transforms microglia and reduces amyloid. Driving gamma oscillations via optogenetics or noninvasive sensory flicker in mouse models of AD results in reduced amyloid β levels and transformed microglia, the primary immune cells in the brain. Following gamma stimulation, microglia's cell bodies become larger, their process length is reduced, and more microglia engulf amyloid β. Image courtesy of Walter Rich.