BRAIN NETWORKING

Question 1

Striatum of Basal Ganglia

Answer 1

putamen and caudate nucleus

Question 2

Basal ganglia contributes to:

Answer 2

– Action selection
– Reinforcement learning

Nearly all of the cerebral cortex projects to the striatum except for the primary visual cortex and primary auditory cortex

Question 3

Increased striatal activity can disinhibit thalamus (via direct pathway)

Answer 3

Striatum inhibits GP internal segment, which removes inhibition of thalamus

Question 4

Hyperdirect pathway

Answer 4

cortex to subthalamic nucleus

Question 5

Direct pathway

Answer 5

striatum to GP internal segment

Question 6

Indirect pathway

Answer 6

Striatum to GP external segment to subthalamic nucleus to GP internal segment

Question 7

Cerebellum plays a role in more automatic execution during/after skill learning

Answer 7

Cerebellum involved in both motor and cognitive functions

Question 8

Cerebellum receives copies of commands from motor and prefrontal cortex

Answer 8

Copies of commands called “efference copies”

Question 9

Cerebellum may output predicted sensory consequence of movements?

Answer 9

Cerebellum may predict new state of body based on efference copy?

Question 10

Hippocampus functions

Answer 10

– Episodic memory
– Spatial navigation

Question 11

Parahippocampal areas

Answer 11

parahippocampal cortex,
perirhinal cortex, entorhinal cortex

Question 12

“Six degrees of separation”

Answer 12

Idea that everyone can be connected in ≤ 6 steps

“The small world problem”

Question 13

Regular network

Answer 13

Every node (dot above) connected to its nearest neighbors (nearby dots)

Question 14

Random network

Answer 14

Increase disorder by reconnecting edges to random nodes until all edges are wired randomly

Connection is called “edge”

Question 15

Small-world network

Answer 15

High clustering like regular graph, yet small characteristic path length (Global average of all distances) like random graph

Question 16

Module

Answer 16

a subset of nodes with high within-module connectivity and low inter-module connectivity

Question 17

Path length

Answer 17

minimum number of edges to go from one node to another

Question 18

Node degree

Answer 18

Number of connections that link a node to the rest of the network

Question 19

Clustering coefficient

Answer 19

Number of connections that exist between nearest neighbors of a node (as a proportion of the maximum number of possible connections)

Question 20

Rich-club architecture

Answer 20

Type of small world network evident in the brain

Question 21

Rich node

Answer 21

Node with a large number of connections, i.e., high-degree node (called network hub)

Question 22

Rich club

Answer 22

Rich nodes that are well-connected with each other, forming a tight subgraph

Question 23

Rich-club organization

Answer 23

Greater likelihood of high-degree nodes forming clubs than low-degree nodes

Question 24

Anatomical connections

Answer 24

– Axon projects from one neuron to another
– Parallel projections of axons form white matter paths in the brain

Question 25

How to measure anatomical connections

Answer 25

– Tracer studies (invasive): Tracer molecule injected into brain and travels along axons – Diffusion MRI (non-invasive): Infer direction of white matter path based on water diffusion

Question 26

Functional connections

Answer 26

– Correlated neural activity between different brain areas – Functional connection may reflect direct or indirect anatomical path between brain areas

Question 27

How to measure functional connections

Answer 27

– Statistical dependencies: correlation, coherence (correlation between oscillation frequencies)… – Can measure functional connections based on spike rate, local field potentials, EEG, functional MRI (BOLD activity)

Question 28

Diffusion MRI

Answer 28

Water diffuses more readily along connections between brain areas Measure water diffusion in each brain voxel Connect voxels based on preferred diffusion directions | Diffusion MRI techniques include DTI or diffusion tensor imaging

Question 29

Functional MRI

Answer 29

Parcellate brain into regions of interest (ROIs) Calculate correlation between BOLD activity in two ROIs Repeat correlation calculation for all possible pairs of ROIs

Question 30

Resting-state functional MRI

Answer 30

Subject scanned with no behavioral task ## Footnote Functional connectivity is time-dependent and modulated by task context

Question 31

Schizophrenia

Answer 31

characterized by delusions, hallucinations, disorganized speech, other symptoms that cause social or occupational dysfunction

Question 32

position invariance

Answer 32

can identify an object no matter where it is | Sensor with big receptive field

Question 33

How to operate effectively on multiple spatial scales?

Answer 33

– How to localize and identify small objects or parts of objects? – How to identify big objects or scenes? – How to identify objects wherever they are (position invariance)?

Question 34

topographic maps | Multiple representations of the environment

Answer 34

– Representation of our environment built from small receptive fields – Representation of our environment built from big receptive fields – Representation of the environment built from different stimulus features, e.g., motion – Orderly representation of sensory space – Different neurons have receptive fields covering different parts of sensory space – Neurons arranged such that nearby neurons represent nearby regions of sensory space and distant neurons represent distant regions of sensory space

Question 35

How to build receptive fields of different sizes?

Answer 35

– Small receptive fields usually found at early stages of sensory pathways near sensory organs – Bigger receptive fields can be built from small receptive fields – If neurons with small, adjacent receptive fields all provide input to the same neuron, then summing these inputs will produce a bigger receptive field

Question 36

Somatosensory RF

Answer 36

– area of body surface – smallest RFs for finger tips – largest RFs for thigh/calf | Mapped along dimensions of body surface

Question 37

Visual RF

Answer 37

– area of visual space – smallest RFs only a few minutes of arc (like dot on page) – largest RFs tens of degrees (like entire page of book) | Mapped along (usually 2) dimensions of the space around you

Question 38

receptive fields (RFs)

Answer 38

part of sensory world to which neuron responds

Question 39

Olfactory receptive field

Answer 39

Mapped along dimension of carbon chain length of odorant

Question 40

Numerical receptive field | space on an abstract scale

Answer 40

Found in some parietal and prefrontal neurons | Mapped along dimension of numerosity

Question 41

big receptive fields

Answer 41

– to identify objects – for position invariance

Question 42

small receptive fields

Answer 42

– to identify detailed features of an object – for high acuity

Question 43

Fovea

Answer 43

part of retina with high spatial resolution – Certain parts of sensory space may occupy disproportionately large part of map, e.g., fovea – This allows greater sensitivity for those parts of sensory space | central part of visual field

Question 44

Brain areas commonly defined by their representation of sensory space

Answer 44

– Sensory space is mapped multiple times in the brain – E.g., there are multiple visual maps of the space around us – Each distinct visual brain area (V1, V2, etc) contains a complete representation of half of visual space (called “hemifield”) – I.e., visual area in left hemisphere predominantly represents right visual field (and vice versa)

Question 45

Retinotopic map

Answer 45

– orderly representation of visual space (hemifield) – called “retinotopic” as it reflects organization of retina | Hemifield = half of visual space

Question 46

Tonotopic map | auditory brain areas

Answer 46

orderly representation of sound (tone) frequency

Question 47

Somatotopic map

Answer 47

orderly representation of body surface

Question 48

Efficient design to group neurons together that are highly interconnected

Answer 48

– Neurons processing nearby sensory space will interact more often (than with other neurons) – Grouping these neurons together reduces wiring

Question 49

connections within a map?

Answer 49

– Each neuron in a map is connected with a subset of the other neurons – If the number of connections of each neuron is held constant, then the proportion of the map that each neuron connects with depends on the number of neurons in the map

Question 50

Large number of neurons in fine-grained maps | small RFs

Answer 50

– Each neuron requires more and longer connections? or – Each neuron connects with fewer neurons?

Question 51

Fewer neurons in coarse-grained map | big RFs

Answer 51

– Neurons representing distal parts of the map more readily connected – Facilitates comparison/integration of information from different parts of the map