Alzheimer’s Disease (AD), one of the most common forms of dementia, is a complex progressive neurodegenerative condition believed at least in part to be caused by an abnormal buildup of proteins in and around brain cells (Ashraf & So, 2020; Du et al., 2018; Theofilas et al., 2018). As these cells become affected, brain tissue atrophies, resulting in a decrease of neurotransmission and synaptic activity between different cortical regions (Gouras, 2019; Zubair, 2019). The hypotheses about the causes of AD are many, and due to the complexity of the human brain, detailed pathogenesis of the disease remains unclear. To better understand the pathophysiological processes of AD, electroencephalograms (EEGs) offer researchers a cost-effective, non-invasive tool for the detection and comparison of normal and anomalous brain activity and is becoming increasingly popular for identifying and quantifying changes in the human brain with respect to memory decline and neurodegenerative disorders (Al-Qazzaz et al., 2014; Bennys et al., 2001; Peters et al., 2016).
There remains ample research available on the existence of neural loops and event-related potentials in large-scale brain networks that support memory (Horn et al., 2012; Ray et al., 2020; Xiang et al., 2009). Event-related potentials (ERPs) are tiny measurable voltages generated in and throughout brain structures as a direct response to a specific sensory, motor, or cognitive event (Blackwood & Muir, 1990; Nunez & Srinivasan, 2006). As such, electrophysiology provides a real-time readout of neural function and network activity, allowing brain-wave measurements and brain scans of various stimuli in the experimental condition to potentially serve as functional biomarkers for AD, particularly at the group and individual level (Babiloni et al., 2020).
Neural Networks and Episodic Memory
The study of large-scale brain networks provides researchers a powerful paradigm for investigating neurological disorders by offering an inclusive physiologic architecture by which to explore integration. Executive control does far more than inhibit automatic responses, it influences working and episodic memory, mediates adaptive response, and supports a range of other executive functions by way of distributed brain networks (Ray et al., 2020). Episodic memory is the neurocognitive system that enables human beings to recall past experiences. It specifically relates to personally experienced events that evaluate memory in the context of recognition, which refers to the judgment that a stimulus event has been previously experienced (Curran et al., 2007). Thus, episodic memory exemplifies the memory of everyday events that can be summoned and explicitly detailed. Studies regarding episodic memory provide researchers valuable insight into how information is acquired, organized, and retrieved (Tulving, 2002).
Ray et al. (2020) examine whole-brain modular structures during tasks involving episodic memory demands. Using functional magnetic resonance imaging (fMRI), researchers explore context-dependent brain network reorganization of healthy adults performing tasks involving cognitive control and episodic memory demands. Connections between the brain’s frontal and parietal regions were identified in regulating cognitive control demands during the encoding and retrieval of episodic memory. While their analysis yielded varying levels of network integration (and separation) during these memory-related tasks, their results support the hypothesis that engagement is largely context-driven, and that the frontal and parietal regions flexibly integrate across domains to support control in episodic memory. Cognitive integration in the context of their study suggests a process by where external elements are appropriately assimilated into our cognitive loops resulting in different degrees of connectivity demanded by encoding and retrieval during episodic memory processing (Ray et al., 2020). Such studies provide insight into how brain networks flexibly organize and reorganize to support control during various tasks across a variety of cognitive domains. Li et al. (2020) investigate how humans process auditory and visual stimuli through the integration of neural systems dedicated specifically to multisensory (or multimodal) information. Employing fMRI, their goal was to identify and localize the activation of multisensory-specific regions during categorical learning. Their findings show that brain regions once considered playing specific roles- or unimodal tasks- are oftentimes resourced outside their primary functional domains. Nee et al. (2012) examine the meta-analysis of the executive components of short-term working memory. The full sample of their study consisted of data from 36 experiments reporting 461 activation foci that revealed a “broad network of medial and lateral frontal and parietal regions involved in the executive processing of working memory” (Nee et al., 2012, p. 269). Researchers have long investigated age-related frontal and parietal scalp ERPs during bottom-up and top-down processing (Friedman et al., 1997; Li et al., 2013; Müller & Knight, 2002; Pehlivanoglu et al., 2020). Outcomes in such works on memory offer evidence that older and younger adults recruit different areas of the brain’s frontal and parietal lobes during bottom-up and top-down processing, with older adults relying on a more frontally distributed network (Buckner, 2004). The results of these findings suggest an organization of working memory by function; and through the process of neural association/ dissociation (analogous to the work by Ray et al. (2020)), the conceptualization of executive processes being network-based becomes palpable.
The Frontoparietal Network (FPN)
In a seminal work by Posner and Dehaene (1994), it was maintained that attention and response in the human brain scarcely lay on one single area. Researchers proposed that specific cognitive processes are mediated by relative electrical activity, are context-dependent, and found in specialized cortical areas spanning the frontal and parietal lobes. The Frontoparietal Network (FPN) is hypothesized to act as a flexible hub of cognitive control and mediation that can alter its functional connectivity across neural networks based on specific objectives (Chadick & Gazzaley, 2011; Ray et al., 2020; Zanto & Gazzaley, 2013). Most recently, Fischer et al. (2020) sought to identify a common set of structures in the FPN involved in memory-guided attention. Their analysis yielded four significant clusters: the angular gyrus, involved in episodic memory encoding and retrieval (Thakral et al., 2017; Tibon et al., 2019; van der Linden et al., 2017), the superior parietal lobes, associated with visual attention (Valdois et al., 2019), the middle frontal gyrus, related to working memory performance, action sets, and decisions (Kamiński et al., 2017; Owens et al., 2018; Rushworth et al., 2004), and the mid-cingulate cortex, active in cognitive control processing (Gruber et al., 2017; Tolomeo et al., 2016). Their findings, supported by network-level interaction effects demonstrated in functional brain imaging, are consistent with the idea that retrieved memories and attentional systems are at least in part mediated by frontoparietal circuits. Such works not only demonstrate but highlight the importance and relevance of both structure and function in complex brain networks.
In cases of dementia, early cognitive deficits in AD patients are seen in episodic memory, which encompasses the encoding, storage, and retrieval of temporally and spatially defined events and the relationships between them (Tulving & Thomson, 1973). Auditory-visual working memory deficits, often ascribed to central executive impairment, are also a recognized feature of AD (Karrasch et al., 2006; Stopford et al., 2012). Located in the temporal lobe and involved in the consolidation of memory and learning, the hippocampus is one of the earliest affected brain regions in AD (Maruszak & Thuret, 2014; Mu & Gage, 2011). In the neocortex system, the hippocampus functions as a rapidly adaptive structure that regulates emotions and captures episodic memories (Kumaran et al., 2016; McClelland et al., 1995; Shastri, 2002). Evidence further suggests the hippocampus contributes to the encoding of visual objects within auditory contexts (Barker & Warburton, 2020; Gottlieb et al., 2010). An article by Eichenbaum (2017) discusses how the prefrontal cortex (PFC) and hippocampus support complementary functions in episodic memory, highlighting direct and indirect prefrontal-hippocampal pathways between the two as being critical for mediating effective memory. These findings support an earlier work by Miller and Cohen (2001) proposing an integrative theory of PFC in cognitive roles such as memory, analogizing the PFC as a switch operator for a host of complementary brain functions. Acknowledging the broad consensus of the hippocampal system in the encoding and retrieval of episodic memories, we seek to further explore these cortical systems underlying hippocampal function that facilitate effective memory outcomes.
A system-based evaluation toward cognition provides insight into how brain networks restructure in supporting various tasks, submitting that the FPN disengages and integrates as needed to support mechanisms in different domains (Ray et al., 2020). The resulting dynamic shifting between executive regions is believed to demonstrate a more concrete representation of information acquisition and analysis suggesting the presence of cognitive flexibility in healthy adults (Spreng & Turner, 2019). A study by Van Buuren et al. (2019) aims to show how functional network interactions at rest underlie individual differences in the memory of healthy young adults. Their findings demonstrate that effective memory hinges not only on the successful connectivity of various cortical networks, but that the strength of those connections translates to better memory performance. It becomes clear that successful and effective memory calls upon a number of cognitive processes spanning large-scale cortical networks. ERP components that reflect the connection process across brain regions during the course of multisensory integration mark this activity.
N4/P6 and Memory
N4 ERP amplitudes are widely accepted as being functionally sensitive in matters of semantic activation, recognition memory, predictive processing, attention, and discourse (Cheyette & Plaut, 2017). Contemplating a multitude of parameters, a significant number of AD studies found lower N4 amplitude in AD diagnosed patients compared to healthy older adult controls (Auchterlonie et al., 2002; Grieder et al., 2013; Olichney et al., 2006; Wolk et al., 2005). As the N4 is arguably one of the more robust measures of brain activity that underscores the use of semantic memory (Kutas & Federmeier, 2000), the present study seeks to examine the degree to which it contributes to episodic memory. Results of earlier studies support the N4 as being highly active in conceptual memory involving pictures and words (Nigam et al., 1992). Recognition memory is a subcategory of declarative memory and expresses the brain’s ability to recognize previously encountered stimuli as being familiar (Rugg & Curran, 2007). From a dual-process perspective, recognition memory is conceptualized as relying on familiarity and recollection (Atkinson & Juola, 1974; Tulving, 1985; Vilberg & Rugg, 2008). In the experimental setting, participants resolve recognition paradigms by making old/new recognition judgments using yes/no tests. Employing scalp-recorded ERPs during such tests enables researchers to measure activity and localization linked to these processes. While the N4 ERP has been associated with recognition memory and familiarity, it has also been linked to how predictability influences memory, specifically how the forecasting of recognition paradigms might impact the encoding of information (Curran, 2000; Paller & Kutas, 1992; Voss & Federmeier, 2011). Because activity across a wide network of brain areas is elicited in the N4 component time window, the highly distributed nature of this neural source makes it suitable for analysis within and between large-scale cortical networks.
The P6 is widely recognized as a language-relevant ERP thought to be elicited by hearing or reading syntactic anomalies, though modern assessments accept that the P6 reflects the general effect of processing difficulty whether it be syntactic or semantic (van Herten et al., 2005). Findings by Shen et al. (2016) validate the existence of semantic P6 localized in executive function areas outside of the language system, thus expanding the application of EEG on the P6 as a possible reliable and sensitive biomarker for other cognitive processes to include memory. Schloerscheidt and Rugg (2004) propose that memory retrieval is driven by multiple neural correlates, and research associates sensitivity of the P6 ERP in indexing memory encoding, particularly across old/new paradigms (Burkhardt, 2007; Olichney et al., 2006). A recent study by Andreau et al. (2020) looks at brain computations involved in visual stimuli target recognition finding a P6-like component related to recognition-based memory retrieval. In clinical trials, subjects diagnosed with mild AD demonstrated lower amplitudes and amplified latencies in ERPs associated with working memory, attention, and executive function (Cecchi et al., 2015; Olichney et al., 2008). These findings suggest complex relationships between working memory and other cognitive processes in the context of ERP components, particularly the late-positive P6 (Guillem et al., 1995; O'Rourke, 2013). The P6 is shown to function as a binder for stimuli in memory recognition tasks involving previously encountered items (Xia et al., 2020). Such discoveries suggest the P6 does far more than simply index memory events- it might integrate them as well. Even if only acting as a computational partner in the course of multimodal target identification, the P6 plays a relevant role in recognition-based memory retrieval (Andreau et al., 2020).
An investigation of scalp-recorded components of both the N4 and P6 ERPs enables a comprehensive analysis of some of the more observable and reliable measures of cognitive synchronicity and neural processing across broad cortical networks. These ERP components have been shown to be sensitive to AD-related change, and as such may provide clinically useful markers. Researchers have long suggested that patients having even mild cognitive impairment reveal an increased risk of conversion to AD by the presence of abnormalities in the N4 and P6 amplitude (Kutas & Federmeier, 2009; Olichney et al., 2002; Olichney et al., 2008). Such work demonstrates the predictive strength of N4/P6 anomaly analysis in AD progression over time. As both ERP components are known to be sensitive to irregular declarative memory and semantic processing, the N4 and P6 together might have significant clinical use in the neurological evaluation of episodic memory at the individual and group levels.
The Present Study
Using secondary data from a study conducted by Kilborn et al. (2009) that investigated ERP components as candidate biomarkers for AD, the present study sought to examine specific ERP measures for the purpose of distinguishing AD patients from a group of healthy age-matched controls. By means of EEG analytical software, we assessed the performance of the N4 and P6 ERP components previously reported to be sensitive to AD in early stages (Olichney et al., 2006; 2008). A group of 63 mild, untreated AD patients and a control group of 73 healthy age-matched individuals were compared on amplitudes and latencies of the N4 and P6 ERP. After calculating peak amplitudes and component latencies in their respective time windows and exploring main differences, ERP measurements were evaluated using analyses of variance (ANOVA) procedures for repeated measures. Statistically significant effects for group and memory were followed up by post-hoc comparisons. Based on the literature presented, it was expected healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks; thus, demonstrating healthy cognitive functioning consistent with older healthy adults (Ray et al., 2020; Spreng & Turner, 2019).