The Multiple Correspondence Analysis Method and Brain Functional Connectivity: Its Application to the Study of the Non-linear Relationships of Motor Cortex and Basal Ganglia

The complexity of basal ganglia (BG) interactions is often condensed into simple models mainly based on animal data and that present BG in closed-loop cortico-subcortical circuits of excitatory/inhibitory pathways which analyze the incoming cortical data and return the processed information to the cortex. This study was aimed at identifying functional relationships in the BG motor-loop of 24 healthy-subjects who provided written, informed consent and whose BOLD-activity was recorded by MRI methods. The analysis of the functional interaction between these centers by correlation techniques and multiple linear regression showed non-linear relationships which cannot be suitably addressed with these methods. The multiple correspondence analysis (MCA), an unsupervised multivariable procedure which can identify non-linear interactions, was used to study the functional connectivity of BG when subjects were at rest. Linear methods showed different functional interactions expected according to current BG models. MCA showed additional functional interactions which were not evident when using lineal methods. Seven functional configurations of BG were identified with MCA, two involving the primary motor and somatosensory cortex, one involving the deepest BG (external-internal globus pallidum, subthalamic nucleus and substantia nigral), one with the input-output BG centers (putamen and motor thalamus), two linking the input-output centers with other BG (external pallidum and subthalamic nucleus), and one linking the external pallidum and the substantia nigral. The results provide evidence that the non-linear MCA and linear methods are complementary and should be best used in conjunction to more fully understand the nature of functional connectivity of brain centers

Studying the functional connectivity of the primary motor cortex with the binarized cross recurrence plot: The influence of Parkinson's disease.

Two new recurrence plot methods (the binary recurrence plot and binary cross recurrence plot) were introduced here to study the long-term dynamic of the primary motor cortex and its interaction with the primary somatosensory cortex, the anterior motor thalamus of the basal ganglia motor loop and the precuneous nucleus of the default mode network. These recurrence plot methods: 1. identify short-term transient interactions; 2. identify long-lasting delayed interactions that are common in complex systems; 3. work with non-stationary blood oxygen level dependent (BOLD) data; 4. may study the relationship of centers with non-linear functional interactions; 5 may compare different experimental groups performing different tasks. These methods were applied to BOLD time-series obtained in 20 control subjects and 20 Parkinson´s patients during the execution of motor activity and body posture tasks (task-block design). The binary recurrence plot showed the task-block BOLD response normally observed in the primary motor cortex with functional magnetic resonance imaging methods, but also shorter and longer BOLD-fluctuations than the task-block and which provided information about the long-term dynamic of this center. The binary cross recurrence plot showed short-lasting and long-lasting functional interactions between the primary motor cortex and the primary somatosensory cortex, anterior motor thalamus and precuneous nucleus, interactions which changed with the resting and motor tasks. Most of the interactions found in healthy controls were disrupted in Parkinson's patients, and may be at the basis of some of the motor disorders and side-effects of dopaminergic drugs commonly observed in these patients

The organization of the basal ganglia functional connectivity network is non-linear in Parkinson's disease

The motor symptoms in Parkinson's disease (PD) have been linked to changes in the excitatory/inhibitory interactions of centers involved in the cortical-subcortical closed-loop circuits which connect basal ganglia (BG) and the brain cortex. This approach may explain some motor symptoms of PD but not others, which has driven the study of BG from new perspectives. Besides their cortical-subcortical linear circuits, BG have a number of subcortical circuits which directly or indirectly connect each BG with all the others. This suggests that BG may work as a complex network whose output is the result of massive functional interactions between all of their nuclei (decentralized network; DCN), more than the result of the linear excitatory/inhibitory interactions of the cortical-subcortical closed-loops. The aim of this work was to study BG as a DCN, and to test whether the DCN behavior of BG changes in PD. BG activity was recorded with MRI methods and their complex interactions were studied with a procedure based on multiple correspondence analysis, a data-driven multifactorial method which can work with non-linear multiple interactions. The functional connectivity of twenty parkinsonian patients and eighteen age-matched controls were studied during resting and when they were performing sequential hand movements. Seven functional configurations were identified in the control subjects during resting, and some of these interactions changed with motor activity. Five of the seven interactions found in control subjects changed in Parkinson's disease. The BG response to the motor task was also different in PD patients and controls. These data show the basal ganglia as a decentralized network where each region can perform multiple functions and each function is performed by multiple regions. This framework of BG interactions may provide new explanations concerning motor symptoms of PD which are not explained by current BG models. Keywords: Parkinson's disease, Basal ganglia, Functional connectivity, Multiple correspondence analysis, Motor disorder

Striatal astrocytes engulf dopaminergic debris in Parkinson's disease: A study in an animal model

<div><p>The role of astrocytes in Parkinson’s disease is still not well understood. This work studied the astrocytic response to the dopaminergic denervation. Rats were injected in the lateral ventricles with 6-hydroxydopamine (25μg), inducing a dopaminergic denervation of the striatum not accompanied by non-selective tissue damage. The dopaminergic debris were found within spheroids (free-spheroids) which retained some proteins of dopaminergic neurons (e.g., tyrosine hydroxylase, the dopamine transporter protein, and APP) but not others (e.g., α-synuclein). Free-spheroids showed the initial (LC3-autophagosomes) but not the late (Lamp1/Lamp2-lysosomes) components of autophagy (incomplete autophagy), preparing their autophagosomes for an external phagocytosis (accumulation of phosphatidylserine). Free-spheroids were penetrated by astrocyte processes (fenestrated-spheroids) which made them immunoreactive for GFAP and S100β, and which had some elements needed to continue the debris degradation (Lamp1/Lamp2). Finally, proteins normally found in neurons (TH, DAT and α-synuclein) were observed within astrocytes 2–5 days after the dopaminergic degeneration, suggesting that the intracellular contents of degenerated cells had been transferred to astrocytes. Taken together, present data suggest phagocytosis as a physiological role of striatal astrocytes, a role which could be critical for cleaning striatal debris during the initial stages of Parkinson’s disease.</p></div

Free-spheroids and autophagy.

<p>The ubiquitin (Ubi) immunoreactivity in spheroids is shown in the A figures. A1 shows three spheroids with marked Ubi immunoreactivity (A2, A3). The distribution of Ubi immunoreactivity in one of these spheroids is shown in A4. Thus, the Ubi and TH immunoreactivity computed in spheroids (n = 200) vs. those recorded in the extracellular space is shown in A5. A6 and A7 show two characteristic examples of Ubi-TH co-localization computed in the denervated striatum. The LC3 immunoreactivity in spheroids is shown in the B figures. B1 and B3 show two examples of LC3 immunoreactivity in TH+ spheroids (B2 and B4 respectively). The distribution of LC3 immunoreactivity across the line indicated in B4 is shown in B5 (vesicular distribution). B6 shows the LC3-TH co-localization computed in the denervated striatum. The syntaxin-17 (Stx17) immunoreactivity in spheroids is shown in the C figures. C1 shows five consecutive slices of a TH+ spheroid with no aggregation of Stx17. C2 shows no LC3-TH co-localization computed in the denervated striatum. C3 shows the Stx17 and TH distribution across slice 4 in fig C1. C4 shows the Stx17 and TH immunoreactivity computed in spheroids (n = 200) vs. those recorded in the extracellular space. The LC3 and Lamp2 immunoreactivity in TH+ spheroids is shown in the D figures. D1-D4 and D5-D8 show two consecutive slices with a TH+ spheroid (D4 and D8; white arrow) showing LC3 (D2 and D6) but not Lamp2 (D3 and D7) immunoreactivity. The distribution of both markers in the soma of a striatal cell (useful to verify that the Lamp2 antibody was working) is indicated with a yellow arrow in D1-D3. The LC3, Lamp2 and TH immunoreactivity computed in spheroids (n = 200) vs. those recorded in the extracellular space is shown in D9. There was no LC3-Lamp2 co-localization in spheroids (D10).The phosphatidylserine (PS) immunoreactivity in spheroids is shown in the E figures. An example of a TH+ spheroid showing a marked PS immunoreactivity is shown in E1 and E2 respectively. The distribution and the co-localization of TH and PS immunoreactivity distribution across this spheroid is shown in E3 and E4 respectively. The LC3 and TH immunoreactivity computed in spheroids (n = 200) vs. those recorded in the extracellular space is shown in E5. The LC3-PS co-localization within spheroids (n = 25) is shown in E6. A4, A5, C3, C4, D9, E3 and E5 show the mean ± standard error. PC: Pearson’s correlation; OC: overlap coefficient; CR: co-localization rate.</p

Response to the dopaminergic denervation of the striatum.

<p>Response to the dopaminergic denervation of the striatum.</p

Identification of fenestrated-spheroids.

<p>Figures in A show examples of the position of DAergic spheroids with respect to astrocytic processes 24 (A1) and 48 (A2 and A3) hours after 6OHDA administration. The figures in B show two examples of the position of DAergic spheroids with respect to astrocytic processes 72 hours after 6OHDA administration. B2-B4 show an amplification and rotation of the yellow square shown in B1. B7 and B8 show an amplification and rotation of the blue square shown in B5-B6. Images C2-C9 present different perspectives of the spatial relationship between a spheroid (green) and a process (red) of an astrocyte (C1), showing that the spheroid is crossed by the astrocytic processes (black arrows in C5 and C6) and that the astrocytic crossing process emits an additional process inside the spheroid which progresses longitudinally across the largest diameter of the spheroid (blue arrows in C3 vs. C4, C5 vs. C6 and C8 vs. C9). D1-D4 images show the top (D1, D2) and bottom (D3, D4) perspectives of a spheroid penetrated by an astrocytic process which ramified into two branches inside the spheroid. A spheroid penetrated by multiple astrocytic processes is shown in E1-E3 (the spheroid boundary is drawn with a dotted line in E1 and E3). Figs F1-F4 present a fenestrated spheroid where the co-localization of GFAP, and S100β inside a TH-immunoreactive spheroid is shown.</p

Dopaminergic protein within astrocytes.

<p>A1 shows an astrocytic process (blue arrow) which showed TH immunoreactivity (A2). Red arrows In A1 and A2 show free-spheroids without GFAP immunoreactivity. A3 and A4 show the distribution of GFAP and TH immunoreactivity across the yellow lines shown in A1 and A2 (mean ± standard error of yellow lines). A high TH immunoreactivity was also found in the cell soma of astrocytes. A6 shows an example of an astrocyte soma (yellow arrow) with TH immunoreactivity (A7). A8 (mean ± standard error) shows the TH immunoreactivity in the astrocyte soma (computed in the area between the cell nucleus -surrounded in fig A5 with a yellow circle- and the cell membrane -indicated with a red circle around the GFAP+ area of the astrocyte). In figs A6 and A7, red arrows show free-spheroids or DAergic axons without GFAP immunoreactivity, and blue arrows show astrocytic processes with TH immunoreactivity. B: an example of an astrocyte (B1) which showed TH immunoreactivity in its processes (black arrows in B3-B6) but also in its soma (B2). C1-C3 show an example of TH immunoreactivity (C1) in the soma of an astrocyte which presents S100β immunoreactivity (generally observed in cytoplasmic regions near the nucleus of astrocytes; C2) surrounded by GFAP immunoreactivity (generally observed in peripheral regions of the astrocyte soma; C3). DAT immunoreactivity (green in D2) was found in the cytoplasmic regions of astrocytes near the nucleus (blue) and which also showed TH immunoreactivity (D1). The TH and DAT immunoreactivity was observed in cytoplasmic regions of astrocytes near the nucleus (blue) and between the GFAP+ intermediate filaments which are normally located in more peripheral regions of the cytoplasm (yellow). Figs E1-E3 and E5-E7 show two examples of astrocytes showing TH (E2 and E6) and DAT (E3 and E7) immunoreactivity in the soma of astrocytes (E1 and E5). Both showed a marked subcellular co-localization of TH and DAT (E4 and E8; M1 is the Manders overlap coefficient which indicates the fraction of the marker 1 overlapping with marker 2, and M2 is that of marker 2 overlapping with marker 1 respectively).</p

Syn, APP and BDA transfer to astrocytes.

<p>Syn was detected in astrocyte processes 4 hours (A1/A2), 24 hours (A3/A4) and five days (A5/A6) after 6OHDA administration, showing a Syn-GFAP colocalization (A8, A9 and A11) which was not observed in free-spheroids (A7, A8 and A10). Syn immunoreactivity was also found in the astrocyte soma (A12-A14). The astrocyte soma showed a high immunoreactivity level for GFAP and Syn (A15; values are mean ± standard error of values in the cytoplasm of astrocytes–inside- vs. those in the extracellular space–outside-). B4 and B5 show an example of the App accumulation (B2) in TH+ spheroids (B1) but not in GFAP+ astrocyte processes (B3). Another example of App accumulation in DAergic spheroids but not in astrocyte processes is shown in B6-B8. As shown in B9, App was found in DAergic spheroids but not in astrocytes (mean ± standard error of values normalized according to the extracellular immunoreactivity; n = 400 for astrocyte processes and n = 150 for spheroids). BDA injections in the posterior regions of the medial forebrain bundle filled DAergic cells of the nigrostriatal system (C1/C2 present the soma of a filled DA-ergic neurons and C3/C4 present a filled DAergic axon). BDA was found in striatal TH+ spheroids (C5/C6) of animals administered with BDA 30 days before the 6OHDA administration. C5 and C6 show an example of BDA incorporation into the processes of astrocytes of the DAergic-denervated striatum.</p